Modern automobile testing systems are complexly computerized measure and control systems, and used in automobile vehicle design centers and assembly plants. Their performance is critical but difficult to be monitored ...Modern automobile testing systems are complexly computerized measure and control systems, and used in automobile vehicle design centers and assembly plants. Their performance is critical but difficult to be monitored efficiently and in real time. This paper introduces an Internet based remote monitoring system for automobile testing systems, and the design and the implementation using Web database and Socket techniques.展开更多
The monitoring system of ship cabin based on single chip computer is introduced. The system can inspect the signal circulatively coming from sensors of all kinds, and give alarm when limit is broken. It demonstrated t...The monitoring system of ship cabin based on single chip computer is introduced. The system can inspect the signal circulatively coming from sensors of all kinds, and give alarm when limit is broken. It demonstrated the working principles, hardware block diagram and software flow diagram of the system.展开更多
The teaching process includes ＂teaching＂ and ＂learning＂ of two aspects, and the teaching process of adult higher education is the autonomous learning process of students under the guidance of teachers. It respecti...The teaching process includes ＂teaching＂ and ＂learning＂ of two aspects, and the teaching process of adult higher education is the autonomous learning process of students under the guidance of teachers. It respectively establishes scientific and reasonable ＂teaching＂ quality monitoring system and ＂learning＂ quality monitoring system, to respectively conduct monitoring of the whole teaching process from＂teaching＂ and ＂learning＂ of the two aspects, in order to effectively improve the teaching quality of adult higher education.展开更多
This paper presents a vehicle navigation and monitoring system scheme based on GIS/GPS/GPRS. The whole system consists of vehicle terminal and monitoring center, the monitoring center and the monitoring terminal compr...This paper presents a vehicle navigation and monitoring system scheme based on GIS/GPS/GPRS. The whole system consists of vehicle terminal and monitoring center, the monitoring center and the monitoring terminal comprises a communication server, communication server is only responsible for data storage and forwarding, the monitoring functions are completed by the monitoring terminal. Therefore, after the map matching, control terminal is performed on the device display to achieve real-time monitoring of vehicle location. The monitoring terminal can also be parameters of the moving vehicle accurate location, velocity and state the necessary query and transmission scheduling control instruction to carry out scientific scheduling and management, and improve operation efficiency. If the vehicle encountered an unexpected condition, harm or left the restricted area, it also can send alarm information to the monitoring terminal, and then the terminal can receive assistance and support in time.展开更多
Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings...Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.展开更多
The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process ...The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.展开更多
Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecastin...Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.展开更多
Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reas...Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.展开更多
This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The ...This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.展开更多
In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this stu...In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.展开更多
In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed i...In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.展开更多
A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to t...A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.展开更多
Tropical river ecosystems are increasingly vulnerable to anthropogenic pressures,yet conventional monitoring methods remain inadequate to capture the rapid and complex ecological changes needed for effective conservat...Tropical river ecosystems are increasingly vulnerable to anthropogenic pressures,yet conventional monitoring methods remain inadequate to capture the rapid and complex ecological changes needed for effective conservation.This study presents“Smart River Watch,”a low-cost,IoT-based ecological monitoring system designed for real-time assessment of key water quality parameters—temperature,pH,and turbidity—in tropical river environments.The system combines Arduino Mega microcontrollers and high-precision sensors with ESP32 WiFi for continuous data transmission to cloud and mobile platforms.Field deployment across five ecologically distinct sites along Indonesia’s Martapura River demonstrated strong performance,achieving exceptional accuracy(r>0.99;error<2%)compared to laboratory methods,a 98.7%transmission success rate,and 23.4-hour operational autonomy.The innovation of this research lies in bridging technological accessibility with ecological needs:enabling high-frequency,real-time monitoring that supports early pollution detection,enhances ecological insight,and empowers local communities through user-friendly mobile interfaces.The cost-effectiveness,rapid deployment(15 minutes per site),and community-based usability of the system make it a scalable solution for biodiversity protection and adaptive water resource management in developing regions.These findings highlight a paradigm shift in ecological monitoring—merging digital innovation with ecosystem stewardship to better protect freshwater biodiversity in the face of accelerating environmental change.展开更多
Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective i...Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.展开更多
Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monit...Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends,and can place patients at risk for hypo-and hyperglycemia.Continuous glucose monitors(CGMs)have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose(BG)data,with trend analysis aided by machine learning(ML)algorithms.These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress.Integration of CGM data into electronic health records(EHR)is essential,as it establishes a foundation for future technologic interfaces with artificial intelligence(AI).Challenges in perioperative CGM implementation include equitable device access,protection of patient privacy and data accuracy,ensuring institution of standardized protocols,and financing the cumbersome healthcare costs associated with staff training and technology platforms.This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.展开更多
The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
In 2022,the Russian Federation commenced development of a national system for permafrost monitoring.The conceptual design of this system reflects three objectives:(1)to collect data on the impact of climate change on ...In 2022,the Russian Federation commenced development of a national system for permafrost monitoring.The conceptual design of this system reflects three objectives:(1)to collect data on the impact of climate change on permafrost,(2)to provide data for evaluation of climate-permafrost feedback,and(3)to provide input to a model-based permafrost data assimilation system.It is intended that the system will eventually consist of 30 active layer monitoring sites and 140 boreholes situated near existing weather stations.As of October 2024,the network comprised 38 sites spanning from the High Arctic islands to the Altai Mountains and across western and eastern Siberia.Among these sites,the lowest recorded temperature at the depth of zero annual amplitude is-11.3℃and the minimum active layer thickness is 0.3 m,as observed on the New Siberian Archipelago.In most boreholes,a positive vertical temperature gradient exists below the depth of zero annual amplitude,indicative of ongoing warming of the upper permafrost layer attributable to climate change.The annual maximum active layer thickness is observed in September with only two exceptions:at the High Arctic sites on Franz Josef Land and Wiese Island and in the low-latitude Sayan Mountain region,where maximum thawing is observed at the end of August.Talik was found in boreholes in Salekhard and Altai where the upper boundary of the permafrost is located at depth of 6-10 m.展开更多
The Ground-based Wide-Angle Cameras array necessitates the integration of more than 100 hardware devices,100 servers,and 2500 software modules that must be synchronized within a 3-second imaging cycle.However,the comp...The Ground-based Wide-Angle Cameras array necessitates the integration of more than 100 hardware devices,100 servers,and 2500 software modules that must be synchronized within a 3-second imaging cycle.However,the complexity of real-time,high-concurrency processing of large datasets has historically resulted in substantial failure rates,with an observation efficiency estimated at less than 50%in 2023.To mitigate these challenges,we developed a monitoring system designed to improve fault diagnosis efficiency.It includes two innovative monitoring views for“state evolution”and“transient lifecycle”.Combining these with“instantaneous state”and“key parameter”monitoring views,the system represents a comprehensive monitoring strategy.Here we detail the system architecture,data collection methods,and design philosophy of the monitoring views.During one year of fault diagnosis experimental practice,the proposed system demonstrated its ability to identify and localize faults within minutes,achieving fault localization nearly ten times faster than traditional methods.Additionally,the system design exhibited high generalizability,with possible applicability to other telescope array systems.展开更多
The Dazu Rock Carvings in Chongqing were inscribed on the World Heritage List in 1999.In recent years,the Dazu Rock Carvings have faced environmental challenges such as geological forces,increased precipitation,pollut...The Dazu Rock Carvings in Chongqing were inscribed on the World Heritage List in 1999.In recent years,the Dazu Rock Carvings have faced environmental challenges such as geological forces,increased precipitation,pollution and tourism,which have led to rock deterioration and structural instability.The multi-source monitoring system for the protection of the rock carvings,based on the Internet of Things,includes Global Navigation Satellite System(GNSS)displacement monitoring,static level displacement monitoring,laser rangefinder displacement monitoring,roof pressure sensor monitoring and environmental damage monitoring.This paper analyses data from each sub-monitoring system within the multi-source monitoring system applied to Yuanjue Cave in the Dazu Rock Carvings.Initially,a correlation analysis between climate monitoring data and roof displacement data was carried out to assess the effect of temperature.Based on the results of the analysis,a temperature correction equation for the laser rangefinder was derived to improve the laser rangefinder displacement monitoring system.The improved system was then used to monitor Cave 168,revealing the deformation and erosion patterns of the roof.The research results demonstrate that the multiparameter monitoring system is capable of accurately measuring and analyzing the stability of the Dazu stone carvings,as well as the effects of environmental conditions on them.The use of the Internet of Things(IoT)and real-time data collection to monitor rock deformation and environmental conditions is an innovative application of technology in cultural heritage conservation.Interpretation of the monitoring system and statistical correlation analysis of temperature and laser rangefinder data highlight the thoroughness of the methodology in this paper and its relevance to sustainable mountain development.In the future,multi-source monitoring systems will have a broader application in the conservation of other UNESCO World Heritage Sites.展开更多
文摘Modern automobile testing systems are complexly computerized measure and control systems, and used in automobile vehicle design centers and assembly plants. Their performance is critical but difficult to be monitored efficiently and in real time. This paper introduces an Internet based remote monitoring system for automobile testing systems, and the design and the implementation using Web database and Socket techniques.
文摘The monitoring system of ship cabin based on single chip computer is introduced. The system can inspect the signal circulatively coming from sensors of all kinds, and give alarm when limit is broken. It demonstrated the working principles, hardware block diagram and software flow diagram of the system.
文摘The teaching process includes ＂teaching＂ and ＂learning＂ of two aspects, and the teaching process of adult higher education is the autonomous learning process of students under the guidance of teachers. It respectively establishes scientific and reasonable ＂teaching＂ quality monitoring system and ＂learning＂ quality monitoring system, to respectively conduct monitoring of the whole teaching process from＂teaching＂ and ＂learning＂ of the two aspects, in order to effectively improve the teaching quality of adult higher education.
文摘This paper presents a vehicle navigation and monitoring system scheme based on GIS/GPS/GPRS. The whole system consists of vehicle terminal and monitoring center, the monitoring center and the monitoring terminal comprises a communication server, communication server is only responsible for data storage and forwarding, the monitoring functions are completed by the monitoring terminal. Therefore, after the map matching, control terminal is performed on the device display to achieve real-time monitoring of vehicle location. The monitoring terminal can also be parameters of the moving vehicle accurate location, velocity and state the necessary query and transmission scheduling control instruction to carry out scientific scheduling and management, and improve operation efficiency. If the vehicle encountered an unexpected condition, harm or left the restricted area, it also can send alarm information to the monitoring terminal, and then the terminal can receive assistance and support in time.
基金supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications(No.2024Y036)the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications(No.2024Z007)the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications(2025).
文摘Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The“monitoring-warning-improvement”loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
基金support of the Korea Institute of Industrial Technol-ogy as“Development of a remote manufacturing system for high-risk,high-difficulty pipe production processes”(kitech EH-25-0004)supported by the Technology Innovation Program(or Industrial Strategic Technology Development Program)(RS-2023–00237714+2 种基金Development of Dynamic Metrology Tool for CMP Process StabilizationRS-2025–02634755Development of Real-Time Electrical Fire Prevention System Technology Reflecting the Characteristics of Traditional Markets)funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea).
文摘The composite material layering process has attracted considerable attention due to its production advantages,including high scalability and compatibility with a wide range of raw materials.However,changes in process conditions can lead to degradation in layer quality and non-uniformity,highlighting the need for real-time monitoring to improve overall quality and efficiency.In this study,an AI-based monitoring system was developed to evaluate layer width and assess quality in real time.Three deep learning models Faster Region-based Convolutional Neural Network(R-CNN),You Only Look Once version 8(YOLOv8),and Single Shot MultiBox Detector(SSD)were compared,and YOLOv8 was ultimately selected for its superior speed,flexibility,and scalability.The selected model was integrated into a user-friendly interface.To verify the reliability of the system,bead width control experiments were conducted,which identified feed speed and extrusion speed as the key process parameters.Accordingly,a Central Composite Design(CCD)experimental plan with 13 conditions was applied to evaluate layer width and validate the system’s reliability.Finally,the proposed system was applied to the additive manufacturing of an aerospace component,where it successfully detected bead width deviations during printing and enabled stable fabrication with a maximum geometric deviation of approximately 6 mm.These findings demonstrate the critical role of real-time monitoring of layer width and quality in improving process stability and final product quality in composite material additive manufacturing.
基金supported by the Collaborative Innovation Center Project of Guangdong Academy of Agricultural Sciences,China(XTXM202202).
文摘Cruciferous vegetables are important edible vegetable crops.However,they are susceptible to various pests during their growth process,which requires real-time and accurate monitoring of these pests for pest forecasting and scientific control.Hanging yellow sticky boards is a common way to monitor and trap those pests which are attracted to the yellow color.To achieve real-time,low-cost,intelligent monitoring of these vegetable pests on the boards,we established an intelligent monitoring system consisting of a smart camera,a web platform and a pest detection algorithm deployed on a server.After the operator sets the monitoring preset points and shooting time of the camera on the system platform,the camera in the field can automatically collect images of multiple yellow sticky boards at fixed places and times every day.The pests trapped on the yellow sticky boards in vegetable fields,Plutella xylostella,Phyllotreta striolata and flies,are very small and susceptible to deterioration and breakage,which increases the difficulty of model detection.To solve the problem of poor recognition due to the small size and breaking of the pest bodies,we propose an intelligent pest detection algorithm based on an improved Cascade R-CNN model for three important cruciferous crop pests.The algorithm uses an overlapping sliding window method,an improved Res2Net network as the backbone network,and a recursive feature pyramid network as the neck network.The results of field tests show that the algorithm achieves good detection results for the three target pests on the yellow sticky board images,with precision levels of 96.5,92.2 and 75.0%,and recall levels of 96.6,93.1 and 74.7%,respectively,and an F_(1) value of 0.880.Compared with other algorithms,our algorithm has a significant advantage in its ability to detect small target pests.To accurately obtain the data for the newly added pests each day,a two-stage pest matching algorithm was proposed.The algorithm performed well and achieved results that were highly consistent with manual counting,with a mean error of only 2.2%.This intelligent monitoring system realizes precision,good visualization,and intelligent vegetable pest monitoring,which is of great significance as it provides an effective pest prevention and control option for farmers.
基金supported by the National Natural Science Foundation of China(Nos.12172248,12302022,12021002,and 12132010)the Tianjin Research Program of Application Foundation and Advanced Technology of China(No.23JCZDJC00950)。
文摘Belt conveyors are extensively utilized in mining and power industries.In a typical coal mine conveyor system,coal is transported over distances exceeding 2 km,involving more than 20000 idlers,which far exceeds a reasonable manual inspection capacity.Given that idlers typically have a lifespan of 1-2 years,there is an urgent need for a rapid,cost-effective,and intelligent safety monitoring system.However,current embedded systems face prohibitive replacement costs,while conventional monitoring technologies suffer from inefficiency at low rotational speeds and lack systematic structural optimization frameworks for diverse idler types and parameters.To address these challenges,this paper introduces an integrated,on-site detachable self-powered idler condition monitoring system(ICMS).This system combines energy harvesting based on the magnetic modulation technology with wireless condition monitoring capabilities.Specifically,it develops a data-driven model integrating convolutional neural networks(CNNs) with genetic algorithms(GAs).The conventional testing results show that the data-driven model not only significantly accelerates the parameter response time,but also achieves a prediction accuracy of 92.95%.The in-situ experiments conducted in coal mines demonstrate the system's reliability and monitoring functionality under both no-load and fullload conditions.This research provides an innovative self-powered condition monitoring solution and develops an efficient data-driven model,offering feasible online monitoring approaches for smart mine construction.
基金supported by the National Natural Science Foundation of China(12064027,12464010)2022 Jiangxi Province High-level and Highskilled Leading Talent Training Project Selected(No.63)+1 种基金Jiujiang"Xuncheng Talents"(No.JJXC2023032)Jiujiang Natural Science Foundation Project(Key Technologies Research on Autonomous Cruise Solar-Powered UAV-2025-1).
文摘This study presents a wireless photovoltaic fault monitoring system integrating an STM32 microcontroller with an Improved Horned Lizard Optimization Algorithm(IHLOA)and a Multi-Layer Perceptron(MLP)neural network.The IHLOA algorithm introduces three key innovations:(1)chaotic initialization to enhance population diversity and global search capability,(2)adaptive random walk strategies to escape local optima,and(3)a cross-strategy mechanism to accelerate convergence and enhance fault detection accuracy and robustness.The system comprises both hardware and software components.The hardware includes sensors such as the BH1750 light intensity sensor,DS18B20 temperature sensor,and INA226 current and voltage sensor,all interfaced with the STM32F103C8T6 microcontroller and the ESP8266 module for wireless data transmission.The software,developed using QT Creator,incorporates an IHLOA-MLP model for fault diagnosis.The user-friendly interface facilitates intuitive monitoring and scalability for multiple systems.Experimental validation on a PV array demonstrates that the IHLOA-MLP model achieves a fault detection accuracy of 94.55%,which is 2.4%higher than the standard MLP,while reducing variance by 63.64%compared to the standard MLP.This highlights its accuracy and robustness.When compared to other optimization algorithms such as BKA-MLP(94.10%accuracy)and HLOA-MLP(94.00%accuracy),the IHLOA-MLP further reduces variance to 0.08,showcasing its superior performance.The system selects voltage as a feature vector to maintain circuit stability,avoiding efficiency impacts from series current sensors.This combined hardware and software approach further reduces false alarms to 0.1%through a consecutive-judgment mechanism,significantly enhancing practical reliability.This work provides a cost-effective and scalable solution for improving the stability and safety of PV systems in real-world applications.
文摘In order to address the current inability of screen printing to monitor printing pressure online,an online printing pressure monitoring system applied to screen printing machines was designed in this study.In this study,the consistency of printed electrodes was measured by using a confocal microscope and the pressure distribution detected by online pressure monitoring system was compared to investigate the relationship.The results demonstrated the relationship between printing pressure and the consistency of printed electrodes.As printing pressure increases,the ink layer at the corresponding position becomes thicker and that higher printing pressure enhances the consistency of the printed electrodes.The experiment confirms the feasibility of the online pressure monitoring system,which aids in predicting and controlling the consistency of printed electrodes,thereby improving their performance.
基金Research Committee,National Technical University of Athens。
文摘In this study,the design and development of a sensor made of low-cost parts to monitor inclination and acceleration are presented.Αmicro electro-mechanical systems,micro electro mechanical systems,sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceleration monitoring node.Online capabilities accessible by mobile phone such as real-time graph,early warning notification,and database logging were implemented using Python programming.The sensor response was calibrated for inherent bias and errors,and then tested thoroughly in the laboratory under static and dynamic loading conditions beside high-quality transducers.Satisfactory accuracy was achieved in real time using the Complementary Filter method,and it was further improved in LabVIEW using Kalman Filters with parameter tuning.A sensor interface with LabVIEW and a 600 MHz CPU microcontroller allowed real-time implementation of highspeed embedded filters,further optimizing sensor results.Kalman and embedded filtering results show agreement for the sensor,followed closely by the lowcomplexity complementary filter applied in real time.The sensor's dynamic response was also verified by shaking table tests,simulating past recorded seismic excitations or artificial vibrations,indicating negligible effect of external acceleration on measured tilt;sensor measurements were benchmarked using highquality tilt and acceleration measuring transducers.A preliminary field evaluation shows robustness of the sensor to harsh weather conditions.
基金The Scientific Research Projects Coordination Unit of Akdeniz University(Türkiye)under contract No.FBA-2022-5542.
文摘A common method for monitoring seawater quality involves collecting samples periodically and analyzing them in a laboratory.This method presents several challenges such as transportation of samples,limited access to testing areas,high costs,and non-instantaneous tests.In this paper,a new Wireless Sensor Network(WSN)based seawater quality monitoring(SQM)system is designed and constructed to observe the seawater parameters that are indicative of marine pollution such as pH,electrical conductivity,temperature,and turbidity,along with geospatial data in real-time.It consists of one master node and several portable sensor nodes that are deployed at different locations on the sea surface.The IEEE 802.15.4 communication standard is utilized between master node and sensor nodes using star topology,while GSM/GPRS is used to connect the master node to a remote server.Collected data from the sensor nodes can be instantly viewed on data grids,graphics,and a map via both a developed web application and a hybrid mobile application.Additionally,the data can be filtered by different parameters and downloaded in spreadsheet format for integration with geographical information systems.After calibrating the sensors,experimental tests were conducted off the coast of Antalya Kucuk Calticak Bay over two separate periods totaling 14 d with only a 2%data loss.Furthermore,a verification test was performed for the sensors,where R-squared values ranged between 0.7 and 1.0,indicating a high correlation between sensor node data and standard instrument data.
文摘Tropical river ecosystems are increasingly vulnerable to anthropogenic pressures,yet conventional monitoring methods remain inadequate to capture the rapid and complex ecological changes needed for effective conservation.This study presents“Smart River Watch,”a low-cost,IoT-based ecological monitoring system designed for real-time assessment of key water quality parameters—temperature,pH,and turbidity—in tropical river environments.The system combines Arduino Mega microcontrollers and high-precision sensors with ESP32 WiFi for continuous data transmission to cloud and mobile platforms.Field deployment across five ecologically distinct sites along Indonesia’s Martapura River demonstrated strong performance,achieving exceptional accuracy(r>0.99;error<2%)compared to laboratory methods,a 98.7%transmission success rate,and 23.4-hour operational autonomy.The innovation of this research lies in bridging technological accessibility with ecological needs:enabling high-frequency,real-time monitoring that supports early pollution detection,enhances ecological insight,and empowers local communities through user-friendly mobile interfaces.The cost-effectiveness,rapid deployment(15 minutes per site),and community-based usability of the system make it a scalable solution for biodiversity protection and adaptive water resource management in developing regions.These findings highlight a paradigm shift in ecological monitoring—merging digital innovation with ecosystem stewardship to better protect freshwater biodiversity in the face of accelerating environmental change.
文摘Due to the existing“island”state of psychological and behavioral data,there is no way for anyone to access students’psychological and behavioral histories.This limits the comprehensive understanding and effective intervention of college students’mental health status.Therefore,this article constructs an artificial intelligence-based psychological health and intervention system for college students.Firstly,this article obtains psychological health testing data of college students through online platforms or on-campus system design,distribution of questionnaires,feedback from close contacts of students,and internal campus resources.Then,the architecture of a mental health monitoring system is designed.Its overall architecture includes a data collection layer,a data processing layer,a decision tree algorithm layer,and an evaluation display layer.The system uses the C4.5 decision tree algorithm to calculate the information gain of the processed sample data,selects the attribute with the maximum value,and constructs a decision tree structure model to evaluate students’mental health.Finally,this article studies the evaluation of students’mental health status by combining multidimensional information such as the SCL-90 scale,self-assessment scale,and student behavior data.Experimental data shows that the system can effectively identify students’mental health problems and provide precise intervention measures based on their situation,with high accuracy and practicality.
文摘Pediatric type 1 diabetes(T1D)is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications.Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends,and can place patients at risk for hypo-and hyperglycemia.Continuous glucose monitors(CGMs)have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose(BG)data,with trend analysis aided by machine learning(ML)algorithms.These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress.Integration of CGM data into electronic health records(EHR)is essential,as it establishes a foundation for future technologic interfaces with artificial intelligence(AI).Challenges in perioperative CGM implementation include equitable device access,protection of patient privacy and data accuracy,ensuring institution of standardized protocols,and financing the cumbersome healthcare costs associated with staff training and technology platforms.This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金supported by the Key Innovative Project of National Importance“Unified National System for Monitoring Climate-active Substances”。
文摘In 2022,the Russian Federation commenced development of a national system for permafrost monitoring.The conceptual design of this system reflects three objectives:(1)to collect data on the impact of climate change on permafrost,(2)to provide data for evaluation of climate-permafrost feedback,and(3)to provide input to a model-based permafrost data assimilation system.It is intended that the system will eventually consist of 30 active layer monitoring sites and 140 boreholes situated near existing weather stations.As of October 2024,the network comprised 38 sites spanning from the High Arctic islands to the Altai Mountains and across western and eastern Siberia.Among these sites,the lowest recorded temperature at the depth of zero annual amplitude is-11.3℃and the minimum active layer thickness is 0.3 m,as observed on the New Siberian Archipelago.In most boreholes,a positive vertical temperature gradient exists below the depth of zero annual amplitude,indicative of ongoing warming of the upper permafrost layer attributable to climate change.The annual maximum active layer thickness is observed in September with only two exceptions:at the High Arctic sites on Franz Josef Land and Wiese Island and in the low-latitude Sayan Mountain region,where maximum thawing is observed at the end of August.Talik was found in boreholes in Salekhard and Altai where the upper boundary of the permafrost is located at depth of 6-10 m.
基金supported by the Young Data Scientist Program of the China National Astronomical Data Center,the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0550401)the National Natural Science Foundation of China(12494573).
文摘The Ground-based Wide-Angle Cameras array necessitates the integration of more than 100 hardware devices,100 servers,and 2500 software modules that must be synchronized within a 3-second imaging cycle.However,the complexity of real-time,high-concurrency processing of large datasets has historically resulted in substantial failure rates,with an observation efficiency estimated at less than 50%in 2023.To mitigate these challenges,we developed a monitoring system designed to improve fault diagnosis efficiency.It includes two innovative monitoring views for“state evolution”and“transient lifecycle”.Combining these with“instantaneous state”and“key parameter”monitoring views,the system represents a comprehensive monitoring strategy.Here we detail the system architecture,data collection methods,and design philosophy of the monitoring views.During one year of fault diagnosis experimental practice,the proposed system demonstrated its ability to identify and localize faults within minutes,achieving fault localization nearly ten times faster than traditional methods.Additionally,the system design exhibited high generalizability,with possible applicability to other telescope array systems.
基金financial support from the National Natural Science Foundation of China(No.42377154)。
文摘The Dazu Rock Carvings in Chongqing were inscribed on the World Heritage List in 1999.In recent years,the Dazu Rock Carvings have faced environmental challenges such as geological forces,increased precipitation,pollution and tourism,which have led to rock deterioration and structural instability.The multi-source monitoring system for the protection of the rock carvings,based on the Internet of Things,includes Global Navigation Satellite System(GNSS)displacement monitoring,static level displacement monitoring,laser rangefinder displacement monitoring,roof pressure sensor monitoring and environmental damage monitoring.This paper analyses data from each sub-monitoring system within the multi-source monitoring system applied to Yuanjue Cave in the Dazu Rock Carvings.Initially,a correlation analysis between climate monitoring data and roof displacement data was carried out to assess the effect of temperature.Based on the results of the analysis,a temperature correction equation for the laser rangefinder was derived to improve the laser rangefinder displacement monitoring system.The improved system was then used to monitor Cave 168,revealing the deformation and erosion patterns of the roof.The research results demonstrate that the multiparameter monitoring system is capable of accurately measuring and analyzing the stability of the Dazu stone carvings,as well as the effects of environmental conditions on them.The use of the Internet of Things(IoT)and real-time data collection to monitor rock deformation and environmental conditions is an innovative application of technology in cultural heritage conservation.Interpretation of the monitoring system and statistical correlation analysis of temperature and laser rangefinder data highlight the thoroughness of the methodology in this paper and its relevance to sustainable mountain development.In the future,multi-source monitoring systems will have a broader application in the conservation of other UNESCO World Heritage Sites.
