Intelligent Monitoring Research Articles

Research on China-Laos Cooperation to Combating Cross-Border Telecom Network Fraud

Telecom network fraud has arisen as a significant transnational crime impacting China and Laos, capitalizing on legal, jurisdictional, and technical discrepancies between the two nations. This paper analyzes the collaborative initiatives between China and Laos to address cross-border telecom network fraud, highlighting significant joint operations carried out from 2016 to 2024. The magnitude of telecom network fraud is shown by the extradition of over 2,500 criminals from Laos to China, which has averted millions of dollars in financial damages via collaborative law enforcement initiatives. The study analyzes significant hurdles, including jurisdictional limitations, technology inadequacies, and political factors that influence the effectiveness of joint operations. This paper proposes improved strategies for real-time digital evidence-sharing, the formation of joint task forces, enhanced financial intelligence monitoring, and expedited extradition processes through the analysis of case studies and ongoing collaborative efforts. By enhancing technological capabilities, offering training, and bolstering political support, China and Laos can establish a more robust framework for addressing telecom network fraud and ensuring regional security.

Intelligent beehive monitoring system based on internet of things and colony state analysis

Bees play a crucial role in terrestrial ecosystems. However, beekeepers are unable to monitor the state of beehives (bees and environment) all the time, which often results in bees escaping or even dying. Currently, some researchers provided the scheme of intelligent beehive monitoring system equipped with the Internet of Things (IoT), There remain two challenges: accurately monitor the environmental status around the hive and accurately track and monitor bees in real time. With the development of the IoT and computer vision algorithms, we hope to provide an automated and efficient system to meet the above challenges. In this paper, we proposed a hive monitoring system, and build a visualization module in the cloud to monitor the activity of bee colonies and the environmental dynamic changes. (1) We proposed a multi-bee tracking algorithm to solve the problem of monitoring bees at the door of the hive; (2) we constructed a dataset containing various complex scenes, named BEE22, for training and testing the performance of our algorithm; (3) we designed a bee counting rule, based on results of multi-bee tracking algorithm, to reasonably count the bees entering or leaving the beehive; (4) we have deployed multiple sensors around(center, margin, door, and environment) the hive to accurately reflect the changes in the environment around the hive. Experimental results demonstrate the effectiveness and excellence of our system. In particular, the tracking performance of the multi-bee tracking algorithm reaches 83.5 % ± 0.7 % Multiple Object Tracking Accuracy (MOTA) and 77.3 %±0.2 % Multiple Object Tracking Precision (MOTP), speeds up to 16 frames per second, compared with other algorithms, MOTA and Identity F1 Score (IDF1) are improved by 5.4 % and 8.2 % respectively. Moreover, our counting algorithm also achieved excellent results, with root mean square error (RMSE) of 1.3 ± 0.1, 0.2 ± 0.0, and 1.6 ± 0.1 in counting the number of bees current, entry, and out scene in an episode, respectively. After that, the system will be deployed and monitored for a long time in the actual scenario, it was found that the activity of bees decreased significantly under heavy rainfall conditions. Additionally, the activity of the bee colony will also increase accordingly, when the amplitude is 500 dB to 2000 dB, the temperature of the center of the beehive is 25 °C to 37 °C, and the humidity is 48 % to 67 %. In summary, our system can provide valuable information for bee farmers to make control decisions on hives.

Significance analysis and evaluation of the impact of a new sustainable ecosystem on the microenvironment of multi-level thermal humidity

Active living walls represent sustainable green technology that improves indoor thermal environment. However, the improvement effect of active living walls in these environments still needs to be further explored, especially with regard to humidity. This study aims to effectively improve the indoor thermal humidity microenvironment of residential buildings, a new sustainable ecosystem (NSES) was developed. NSES integrates plants, spray cooling, and a fan, offering four operation modes, all controlled through intelligent monitoring based on the indoor conditions. Under the working scale of limited space in the microenvironment, evaluating system performance has solved the problem of thermal comfort. In addition, subjective questionnaires are used to evaluate human thermal comfort indicators. The results indicate that (1) Mode 1 exhibits the best performance on the microenvironment throughout the season at a distance of 0.5 m horizontally and 1.1 m vertically from NSES; as the vertical height and horizontal distance increase, performance gradually weakens. (2) Under the conditions of 0.5 m horizontal distance and 1.1 m vertical height, NSES has the best improvement effect on the microenvironment on sunny summer days; an average temperature decrease of 5.6 °C and an average humidity increase of 27.5 % were achieved. (3) NSES can improve human comfort, with thermal comfort zones ranging from 22.4 to 24.3 °C in autumn, and 22.4–23.8 °C in spring. These results highlight the potential value of NSES in improving the microenvironment and enhancing human thermal comfort. This study emphasizes the importance of NSES in appropriately selecting distance, height, season, and climate.

Future Trends and Technological Innovations of Private AI Deployment

As data protection laws like GDPR tighten, companies must ensure their AI systems comply with regulations, leading to widespread use of encryption, federated learning, and differential privacy to protect data. Privatized AI deployment will transform enterprise operations by integrating AI with core business processes, automating and enhancing efficiency and decision-making. For instance, manufacturing firms can use privatized AI for intelligent monitoring and predictive maintenance, reducing costs and improving quality. The market landscape will shift as companies reevaluate technology and service providers, favoring those offering comprehensive privatization solutions for system security and stability. The growing demand for AI expertise will also drive educational institutions to offer more AI-related courses and training. In summary, the trends in privatized AI deployment will deeply affect business models and market structures, necessitating that companies stay abreast of technological advancements and actively embrace privatized AI opportunities for sustained innovation and growth.

Construction of Solution Processable NUS-8/PANI Nanosheets via Template-Directed Polymerization for Ultratrace Gas Sensing.

The advancement of wireless gas sensing signifies a substantial leap forward in gas detection and intelligent monitoring technologies. This necessitates stringent design criteria for gas sensitive materials with good solution processability, conductivity, and porosity, whose design and synthesis remain challenging yet highly sought-after. Herein, the fabrication of NUS-8/polyaniline (PANI) nanosheets is presented with excellent solution processability, high porosity, triboelectric property, and superior electrical conductivity via a template-directed polymerization strategy. Solution processable NUS-8 nanosheets, synthesized directly by a "one-pot" approach, serve as templates to enhance the "on-site" polymerization of aniline, resulting in the formation of PANI layer on NUS-8 nanosheets with a thickness of 7nm. The resultant NUS-8/PANI nanosheets exhibit outstanding solution processability, and a film conductivity of 8.6 Sm-1. The solution processability enables the facile fabrication of homogeneous and compact NUS-8/PANI films and thus their integration onto electronic devices targeted for multifunctional sensing. The NUS-8/PANI coated sensors demonstrate sensitive and selective detection at room temperature toward ultratrace ammonia with a detection limit of 120 ppb. A wireless sensing system based on the NUS-8/PANI-coated sensor is capable to monitor the spoilage process of meat. This study paves novel avenues for designing and synthesizing gas-sensitive materials for practical applications.

Vibration energy-based indicators for multi-target condition monitoring in milling operations

The demand for intelligent process monitoring is increasing in aerospace manufacturing to ensure tight tolerances and high surface quality. Real-time monitoring in machining is crucial for machined accuracy and process reliability, reducing production times and costs, and enhancing automation of the manufacturing process. This study presents a robust multi-target condition monitoring method based on the vibration signals. Firstly, three new energy ratio indicators with dimensionless characteristics were defined for tool wear, breakage, and chatter monitoring. Secondly, the vibration energy loss from the tool tip to the tool holder, and spindle housing was measured and compared, and the rules of vibration loss from the tool tip to the spindle housing were revealed. Using force signals as a reference, the monitoring performance of industrially acceptable acceleration and sound signals in multi-target condition monitoring was quantitatively analyzed. Finally, the performance of the proposed vibration energy-based indicators was experimentally illustrated and quantitatively evaluated. It is shown that these indicators can be used to discriminate between tool breakage and chatter, as well as to assess tool wear. The new monitoring method can also minimize the costs of process monitoring by reducing the use of expensive sensors or overusing multiple sensors in a smart manufacturing system.

EARTHQUAKE-RESISTANT BUILDING DESIGN: INNOVATIONS AND CHALLENGES

This study provides a comprehensive systematic review of innovations in earthquake-resistant building design, focusing on advancements in materials, technologies, and methodologies aimed at enhancing structural resilience. A total of 32 peer-reviewed articles were analyzed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The findings highlight the critical role of advanced materials such as fiber-reinforced polymers (FRPs) and shape memory alloys (SMAs) in improving seismic performance, particularly through enhanced energy dissipation and structural flexibility. Technological integrations like Building Information Modeling (BIM), artificial intelligence (AI), and structural health monitoring (SHM) systems were identified as transformative tools that optimize design processes, predict structural vulnerabilities, and enable real-time risk management. Advanced simulation techniques, including finite element analysis (FEA) and computational fluid dynamics (CFD), were shown to significantly improve the accuracy and efficiency of seismic design. Despite these innovations, challenges related to cost, regulatory inconsistencies, and limited access to cutting-edge technologies persist, particularly in developing regions. The study concludes that while these advancements have revolutionized earthquake-resistant design, further efforts are needed to address these barriers and promote global resilience to seismic hazards.

Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array.

Continuous and reliable monitoring of gait is crucial for health monitoring, such as postoperative recovery of bone joint surgery and early diagnosis of disease. However, existing gait analysis systems often suffer from large volumes and the requirement of special space for setting motion capture systems, limiting their application in daily life. Here, we develop an intelligent gait monitoring and analysis prediction system based on flexible piezoelectric sensors and deep learning neural networks with high sensitivity (241.29 mV/N), quick response (66 ms loading, 87 ms recovery), and excellent stability (R2 = 0.9946). The theoretical simulations and experiments confirm that the sensor provides exceptional signal feedback, which can easily acquire accurate gait data when fitted to shoe soles. By integrating high-quality gait data with a custom-built deep learning model, the system can detect and infer human motion states in real time (the recognition accuracy reaches 94.7%). To further validate the sensor's application in real life, we constructed a flexible wearable recognition system with human-computer interaction interface and a simple operation process for long-term and continuous tracking of athletes' gait, potentially aiding personalized health management, early detection of disease, and remote medical care.

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Design and Application of Intelligent Building Environment Monitoring System Based on Wireless Sensor Network

Methodology for Real-Time Torque Estimation in a Ship Propulsion Digital Twin

Abstract The safe operation of ships requires the condition of propulsion components to be maintained. Digital twins are a promising alternative for intelligent monitoring of these complex systems. Digital twins require models which ensure that the digital representation is able to mimic the behavior of the physical system. Alternate modeling solutions must be found when intellectual property restrictions or lack of available information limit the usability of physics-based models. This paper considers such a case where a system model of the propulsion system requires a real-time capable model of the propeller hydrodynamic torque. The creation of a data-driven hydrodynamic torque model based on full-scale, operational measurements is discussed. The described method focuses on the significant challenges associated with data cleaning and preparation while also evaluating whether well-known machine learning methods are suited for this application. The methods use speed-over-ground, heading, course, rotational speed, and propeller pitch as inputs. The outputs of the models are the single quadrant propeller torque coefficient and the amplitude of harmonic torsional excitation. These outputs are then combined to create a holistic prediction of the torque. Results indicate that both a polynomial least-squares fit and a shallow neural network predict the mean and the amplitude of harmonic components of the torque well. This prediction can be used to isolate the hydrodynamic torque when more than one torque source is present or to simulate what-if scenarios in a digital twin environment.

Survey on machine vision-based intelligent water quality monitoring techniques in water treatment plant: Fish activity behavior recognition-based schemes and applications

Abstract Water is a vital resource essential to the survival and development of all creatures. With the rapid growth of industry and agriculture, people face a severe threat of ecological destruction and environmental pollution while living earthly lives. Water pollution, in particular, harms people’s health the most. As a result, water supply security has become a top priority. As a critical point in water supply safety, monitoring water quality effectively and forecasting sudden water contamination on time has become a research hotspot worldwide. With the rapid development and wide applications of artificial intelligence and computer vision technologies, biological activity identification-based intelligent water quality monitoring methods have drawn widespread attention. They were taking fish activities as the water-quality indicator has gained extensive attention by introducing advanced computer vision and artificial intelligence technologies with low cost and ease of carrying. This article comprehensively reviews recent progress in the research and applications of machine vision-based intelligent water quality monitoring and early warning techniques based on fish activity behavior recognition. In detail, it addresses water quality-oriented fish detection and tracking, activity recognition, and abnormal behavior recognition-based intelligent water quality monitoring. It analyzes and compares the performance and their favorite application conditions. Finally, it summarizes and discusses the difficulties and hotspots of water quality monitoring based on the fish’s abnormal behavior recognition and their future development trends.

Automated monitoring of natural parameters of renewable energy production systems

The paper discusses the urgent issues of the need to automate the monitoring of natural parameters of renewable energy production systems. Monitoring involves the collec- tion and analysis of various parameters of the natural environment that affect the efficiency and environmental friendliness of systems powered by renewable energy. Based on analytical materials and practical work related to techno-economic and ecological studies concerning the assessment of water reserves in indi- vidual wells, the study focused on the development of technologies and technical means for monitoring. It was found that, in practice, hydrogeological research on groundwaters use outdated methods and technical means for measuring and storing data. A local automated system for monitoring well parameters was developed and constructed. It ensures the operation of the hydrothermal heat pump system at the experimental site of the Institute of Renewable Energy of the National Academy of Sciences of Ukraine. This system was tested in our research. The benefits of this study are revealing the need to create an intelligent monitoring system with the possibility of automated decision-making regarding the management of renewable energy generation systems, depending on changes in the am- bient parameters. A network automated system for monitoring natural parameters was developed, consisting of several wireless sensor subsystems that communicate with each other via wireless connection and transmit information to the gateway. The gateway, in turn, is connected to the computer with a graphical interface for interaction with the system. The results of work indicate that the proposed structures for automated water monitoring are simple and easy to implement. Consequently, the study revealed that one of the prospects is the development of automated monitoring systems with intelligent features, capable of making optimal individual decisions regard- ing the management of renewable energy generation systems under variable environmental parameters. As a result, the data obtained from this study have significant scientific and practical implications for advancing the design methodology of geothermal heat pump wells, focusing on long-term forecasts and assessments of their ecological and economic efficiency.

The Wind Power Watch, "Guarding" for Safety - Data-Driven Intelligent Deterrence System for Monitoring Ships in Offshore Wind Farms

This study successfully developed a ship monitoring and repulsion system for offshore wind farms aimed at ensuring the safety and stable operation of the wind farms. The system is capable of real-time monitoring of ship activities and environmental conditions, quickly identifying and eliminating potential safety risks. The research content includes various aspects such as ship detection, path tracking, repulsion decision-making, and data processing. The system integrates multi-sensor data, Geographic Information Systems (GIS), Automatic Identification Systems (AIS) for ships, artificial intelligence algorithms, and advanced communication technologies, constructing an efficient and intelligent monitoring and repulsion framework. Field test results indicate that the system exhibits stable performance and efficient monitoring capabilities under harsh weather conditions, while the intelligent voice repulsion function has shown significant effects.

A Smartphone-Mediated "All-In-One" Biosensing Chip for Visual and Value-Assisted Detection.

A smartphone-mediated self-powered biosensor is fabricated for miRNA-141 detection based on the CRISPR/Cas12a cross-cutting technique and a highly efficient nanozyme. As a novel nanozyme and a signal-amplified coreaction accelerator, the AuPtPd@GDY nanozyme exhibits an excellent ability to catalyze cascade color reactions and high conductivity to enhance the electrochemical signal for miRNA-141 assays. After CRISPR/Cas12a cross-cutting of S2-glucose oxidase (S2-GOD), the electrochemical signal is weakened, and miRNA-141 is detected by monitoring the decrease in the signal. On the other hand, a cascade reaction among glucose, H2O2, and TMB is catalyzed by GOD and AuPtPd@GDY, respectively, resulting in a color change of the solution, which senses miRNA-141. The self-powered biosensor enables value-assisted and visual detection of miRNA-141 with limits of detection of 3.1 and 15 aM, respectively. Based on the dual-modal self-powered sensing system, a smartphone-mediated "all-in-one" biosensing chip is designed to achieve the real-time and intelligent monitoring of miRNA-141. This work provides a new approach to design multifunctional biosensors to realize the visualization and portable detection of tumor biomarkers.

Detection of aberration in human behavior using shallow neural network over smartphone inertial sensors data

AbstractThe integration of different Mobile Edge Computing (MEC) applications has significantly enhanced the realm of security and surveillance, with Human Activity Recognition (HAR) standing out as a crucial application. The diverse sensors found in smartphones have made it convenient for monitoring applications to gather and analyze data, rendering them valuable for HAR purposes. Moreover, MEC can be employed to automate surveillance, allowing intelligent monitoring of restricted areas to identify and respond to unwanted or suspicious activities. This research develops a system using motion sensors in smartphones to identify unusual human activities. People's smartphones were employed to monitor both suspicious and regular activities. Information was collected for various actions categorized as either suspicious or regular. When a person performs a certain action, the smartphone records a series of sensory data, analyse important patterns from the basic data, and then determines what the person is doing by combining information from different sensors. To prepare the data, information from different sensors was aligned to a shared timeline. In this study, we used a sliding window approach on synchronized data to feed sequences into LSTM and CNN models. These models, which include initial layers of LSTM and CNN, automatically find important patterns in the order of human activities. We combined SVM with the features extracted by the shallow Neural Network to make a mixed model that predicts suspicious activities. Lastly, we compared LSTM, CNN, and our new shallow mixed neural network using a new real‐time dataset. The mixed model of CNN and SVM achieved an accuracy of 94.43%. Additionally, the sliding window method's effectiveness was confirmed with a 4.28% improvement in accuracy.

Adopting marketing intelligence to achieve competitive advantage: Applied research in a sample of the union bank of Iraq

The purpose of this research is to investigate how a marketing intelligence system may help an organization get a competitive edge within the study setting and to comprehend the practicalities of doing so. There were two variables in the study: marketing intelligence, which was represented by the dimensions of customer understanding, market analysis, product intelligence, and competitor monitoring, was the independent variable, and competitive advantage, which was represented by the dimensions of cost, quality, flexibility, and delivery, was the dependent variable. Surveys were used in the study to get firsthand information from the Union Bank of Iraq in the province of Baghdad. A deliberate sample including thirty bank managers was chosen. SPSS V.26 software was utilized to process the data utilizing a range of statistical methods. The investigation produced a number of findings. The most notable of them was the direct connection and beneficial impact of the marketing intelligence system on Union Bank of Iraq's ability to gain a competitive edge. A number of recommendations were also made, the most notable of which being that the management of the bank should monitor environmental changes in order to recognize changes in the market, take advantage of opportunities, and address dangers.

Intelligent Monitoring and Optimization of Wind Turbine Operation Status on Basis of Vibration Signals

In response to the large monitoring errors and incomplete consideration of fault locations in the operation status of wind turbines, this paper combined vibration signals and used a model constructed using nonlinear state estimation technology (NSET) to monitor and optimize the operation status of wind turbines. Firstly, it collected relevant SCADA (supervisory control and data acquisition) and vibration sensor data of Unit 5 of Guangdong Yangjiang Shaba Offshore Wind Farm on site, and used wavelet transform to denoise the vibration signal. Then, full consideration can be given to the faulty parts such as wind blades and gearboxes, and a model constructed by NSET using Markov distance to construct a process memory matrix can be used to monitor the status of the wind turbine equipment. Finally, the multi-level fuzzy comprehensive evaluation method can be used to optimize the comprehensive evaluation of the operating status of wind turbines, reducing monitoring errors.

IOT based intelligent energy monitoring of grid connected hybrid systems and controlling of loads using PLC

IOT based intelligent energy monitoring of grid connected hybrid systems and controlling of loads using PLC

Automatic Disease Detection from Strawberry Leaf Based on Improved YOLOv8.

Strawberries are susceptible to various diseases during their growth, and leaves may show signs of diseases as a response. Given that these diseases generate yield loss and compromise the quality of strawberries, timely detection is imperative. To automatically identify diseases in strawberry leaves, a KTD-YOLOv8 model is introduced to enhance both accuracy and speed. The KernelWarehouse convolution is employed to replace the traditional component in the backbone of the YOLOv8 to reduce the computational complexity. In addition, the Triplet Attention mechanism is added to fully extract and fuse multi-scale features. Furthermore, a parameter-sharing diverse branch block (DBB) sharing head is constructed to improve the model's target processing ability at different spatial scales and increase its accuracy without adding too much calculation. The experimental results show that, compared with the original YOLOv8, the proposed KTD-YOLOv8 increases the average accuracy by 2.8% and reduces the floating-point calculation by 38.5%. It provides a new option to guide the intelligent plant monitoring system and precision pesticide spraying system during the growth of strawberry plants.

Research on On-Line Monitoring of Grinding Wheel Wear Based on Multi-Sensor Fusion.

The state of a grinding wheel directly affects the surface quality of the workpiece. The monitoring of grinding wheel wear state can allow one to efficiently identify grinding wheel wear information and to timely and effectively trim the grinding wheel. At present, on-line monitoring technology using specific sensor signals can detect abnormal grinding wheel wear in a timely manner. However, due to the non-linearity and complexity of the grinding wheel wear process, as well as the interference and noise of the sensor signal, the accuracy and reliability of on-line monitoring technology still need to be improved. In this paper, an intelligent monitoring system based on multi-sensor fusion is established, and this system can be used for precise grinding wheel wear monitoring. The proposed system focuses on titanium alloy, a typical difficult-to-process aerospace material, and addresses the issue of low on-line monitoring accuracy found in traditional single-sensor systems. Additionally, a multi-eigenvalue fusion algorithm based on an improved support vector machine (SVM) is proposed. In this study, the mean square value of the wavelet packet decomposition coefficient of the acoustic emission signal, the grinding force ratio of the force signal, and the effective value of the vibration signal were taken as inputs for the improved support vector machine, and the recognition strategy was adjusted using the entropy weight evaluation method. A high-precision grinding machine was used to carry out multiple sets of grinding wheel wear experiments. After being processed by the multi-sensor integrated precision grinding wheel wear intelligent monitoring system, the collected signals can accurately reflect the grinding wheel wear state, and the monitoring accuracy can reach more than 92%.