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
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
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
As the core facility of offshore wind power systems,the structural safety of offshore booster stations directly impacts the stable operation of entire wind farms.With the global energy transition toward green and lowc...As the core facility of offshore wind power systems,the structural safety of offshore booster stations directly impacts the stable operation of entire wind farms.With the global energy transition toward green and lowcarbon goals,offshore wind power has emerged as a key renewable energy source,yet its booster stations face harsh marine environments,including persistent wave impacts,salt spray corrosion,and equipment-induced vibrations.Traditional monitoring methods relying on manual inspections and single-dimensional sensors suffer from critical limitations:low efficiency,poor real-time performance,and inability to capture millinewton-level stress fluctuations that signal early structural fatigue.To address these challenges,this study proposes a biomechanics-driven structural safety monitoring system integrated with deep learning.Inspired by biological stress-sensing mechanisms,the system deploys a distributedmulti-dimensional force sensor network to capture real-time stress distributions in key structural components.A hybrid convolutional neural network-radial basis function(CNN-RBF)model is developed:the CNN branch extracts spatiotemporal features from multi-source sensing data,while the RBF branch reconstructs the nonlinear stress field for accurate anomaly diagnosis.The three-tier architectural design—data layer(distributed sensor array),function layer(CNN-RBF modeling),and application layer(edge computing terminal)—enables a closedloop process from high-resolution data collection to real-time early warning,with data processing delay controlled within 200 ms.Experimental validation against traditional SOM-based systems demonstrates significant performance improvements:monitoring accuracy increased by 19.8%,efficiency by 23.4%,recall rate by 20.5%,and F1 score by 21.6%.Under extreme weather(e.g.,typhoons and winter storms),the system’s stability is 40% higher,with user satisfaction improving by 17.2%.The biomechanics-inspired sensor design enhances survival rates in salt fog(85.7%improvement)and dynamic loads,highlighting its robust engineering applicability for intelligent offshore wind farm maintenance.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
An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The method...An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.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
Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribut...Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes,thereby achieving graded post-earthquake response measures.Design/methodology/approach–The seismic intensity monitoring system for railways adopts a two-level architecture,namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform.The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm.A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.Findings–The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang.During the operational period,the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times.Among these,earthquakes triggering the Kriging interpolation algorithm occurred twice.The system operated stably throughout the application period and successfully visualized relevant seismic impact data,such as earthquake intensity distribution maps and affected railway mileage sections.These results validate the system’s practicality and effectiveness.Originality/value–The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network.It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope,thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality.The system can provide clear graded interval recommendations for post-earthquake disposal,effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.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
A wearable health monitoring system is a promising device for opening the era of the fourth industrial revolution due to increasing interest in health among modern people.Wearable health monitoring systems were demons...A wearable health monitoring system is a promising device for opening the era of the fourth industrial revolution due to increasing interest in health among modern people.Wearable health monitoring systems were demonstrated by several researchers,but still have critical issues of low performance,inefficient and complex fabrication processes.Here,we present the world’s first wearable multifunctional health monitoring system based on flash-induced porous graphene(FPG).FPG was efficiently synthesized via flash lamp,resulting in a large area in four milliseconds.Moreover,to demonstrate the sensing performance of FPG,a wearable multifunctional health monitoring system was fabricated onto a single substrate.A carbon nanotube-polydimethylsiloxane(CNT-PDMS)nanocomposite electrode was successfully formed on the uneven FPG surface using screen printing.The performance of the FPG-based wearable multifunctional health monitoring system was enhanced by the large surface area of the 3D-porous structure FPG.Finally,the FPG-based wearable multifunctional health monitoring system effectively detected motion,skin temperature,and sweat with a strain GF of 2564.38,a linear thermal response of 0.98Ω℃^(-1) under the skin temperature range,and a low ion detection limit of 10μM.展开更多
The intrusion of obstacles onto railway tracks presents a significant threat to train safety,characterized by sudden and unpredictable occurrences.With China leading the world in high-speed rail mileage,ensuring railw...The intrusion of obstacles onto railway tracks presents a significant threat to train safety,characterized by sudden and unpredictable occurrences.With China leading the world in high-speed rail mileage,ensuring railway security is paramount.The current laser monitoring technologies suffer from high false alarm rates and unreliable intrusion identification.This study addresses these issues by investigating high-resolution laser monitoring technology for railway obstacles,focusing on key parameters such as monitoring range and resolution.We propose an enhanced non-uniform laser scanning method,developing a laser monitoring system that reduces the obstacle false alarm rate to 2.00%,significantly lower than the 20%standard(TJ/GW135-2015).This rate is the best record for laser monitoring systems on China Railway.Our system operates seamlessly in all weather conditions,providing superior accuracy,resolution,and identification efficiency.It is the only 3D LiDAR system certified by the China State Railway Group Co.,Ltd.(Certificate No.[2023]008).Over three years,our system has been deployed at numerous points along various lines managed by the China State Railway Group,accumulating a dataset of 300,000 observations.This extensive deployment has significantly enhanced railway safety.The development and implementation of our railway laser monitoring system represent a substantial advancement in railway safety technology.Its low false alarm rate(2.00%),high accuracy(20 cm×20 cm×20 cm),and robust performance in diverse conditions underscore its potential for widespread adoption,promising to enhance railway safety in China and internationally.展开更多
Difficulties in the geometric and performance control of wire laser additive manufacturing have hindered its widespread application.In this study,an in situ process monitoring system that combines a machine vision-bas...Difficulties in the geometric and performance control of wire laser additive manufacturing have hindered its widespread application.In this study,an in situ process monitoring system that combines a machine vision-based interlayer height controller(IHC)and P-controller-based melt pool temperature controller(MTC)was developed to improve the vertical dimensional accuracy and mechanical properties of off-axis fine-wire laser-directed energy deposition(OAFW-LDED)for 316 L thin-walled parts.The IHC effectively mitigates external defect inheritance,while its synergy with the MTC ensures process stability,improving the vertical dimensional accuracy to±0.2 mm.Grain refinement was achieved by controlling the thermal input to optimize the thermal history and heat accumulation.A heterogeneous microstructure with alternating coarse and fine grains was observed and intergranular thermal cracking was suppressed.The yield and tensile strengths increased from 262 to 416 MPa to 313 and 516 MPa,respectively,with improved consistency in the yield strength between the top and bottom sections.However,excessive laser heat input caused interlayer cracks.Conversely,increasing the heat input through substrate preheating did not induce additional cracks and improved the overall hardness consistency of the thin-walled samples.Therefore,this study proposes a new formability control strategy for OAFW-LDEDs of thin-walled parts.展开更多
Protected horticulture makes use of related facilities, engineering technolo- gy and management technologies to create or improve local environment in order to provide optimal environment concerning controllable tempe...Protected horticulture makes use of related facilities, engineering technolo- gy and management technologies to create or improve local environment in order to provide optimal environment concerning controllable temperature, humidity, and light for farming and breeding industry, as well as product storage. Protected horticulture is independent to some extent, instead of relying greatly on nature, targeting full use of soil, climate and biological potential. The research concluded production characteristics of protected horticulture and analyzed the application of protected hor- ticulture intelligent monitoring system in protected greenhouse cultivation. In addition, the future development was proposed on protected horticulture intelligent monitoring system.展开更多
Indonesia is a producer in the fisheries sector,with production reaching 14.8 million tons in 2022.The production potential of the fisheries sector can be optimally optimized through aquaculture management.One of the ...Indonesia is a producer in the fisheries sector,with production reaching 14.8 million tons in 2022.The production potential of the fisheries sector can be optimally optimized through aquaculture management.One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions.IoT technology can be applied to support a fish pond aquaculture monitoring system,especially for catfish species(Siluriformes),in real-time and remotely.One of the technologies that can provide this convenience is the IoT.The problem of this study is how to integrate IoT devices with Firebase’s cloud data system to provide reliable and precise data,which makes it easy for fish cultivators to monitor fishpond conditions in real time and remotely.The IoT aquaculture fishpond monitoring use 3 parameters:(1)water temperature;(2)pHwater level;and(3)turbidity level of pond water.IoT devices use temperature sensors,pH sensors,and turbidity sensors,which are integrated with a microcontroller and Wi-Fi module.Data from sensor readings are sent to the Firebase cloud via theWi-Fi module so that it can be accessed in real time by end users with an Androidbased mobile app.The findings are(1)the IoT-based aquaculture monitoring system device has a low error rate in measuring temprature,pH,and turbidity with a percentage of 1.75%,1.94% and 9.78%,respectively.Overall,the total average error of the three components is 4.49%;(2)in cost analysis,IoT-based has a cost-effectiveness of 94.21% compared to labor costs.An IoT-based aquaculture monitoring system using Firebase can be effectively used as a technology for monitoring fish pond conditions in real-time and remotely for fish cultivators that contribute to providing an IoT-based aquaculture monitoring system that produces valid data,is precise,is easy to implement,and is a low-cost system.展开更多
Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system...Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.展开更多
Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still inv...Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.展开更多
Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew back...Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.展开更多
The original purpose of Vessel Monitoring System(VMS) is for enforcement and control of vessel sailing. With the application of VMS in fishing vessels, more and more population dynamic studies have used VMS data to im...The original purpose of Vessel Monitoring System(VMS) is for enforcement and control of vessel sailing. With the application of VMS in fishing vessels, more and more population dynamic studies have used VMS data to improve the accuracy of fisheries stock assessment. In this paper, we simulated the trawl trajectory under different time intervals using the cubic Hermite spline(c Hs) interpolation method based on the VMS data of 8 single otter trawl vessels(totally 36000 data items) fishing in Zhoushan fishing ground from September 2012 to December 2012, and selected the appropriate time interval. We then determined vessels' activities(fishing or non-fishing) by comparing VMS speed data with the corresponding speeds from logbooks. The results showed that the error of simulated trajectory greatly increased with the increase of time intervals of VMS data when they were longer than 30 minutes. Comparing the speeds from VMS with those from the corresponding logbooks, we found that the vessels' speeds were between 2.5 kn and 5.0 kn in fishing. The c Hs interpolation method is a new choice for improving the accuracy of estimation of sailing trajectory, and the VMS can be used to determine the vessels' activities with the analysis of their trajectories and speeds. Therefore, when the fishery information is limited, VMS can be one of the important data sources for fisheries stock assessment, and more attention should be paid to its construction and application to fisheries stock assessment and management.展开更多
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 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 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.
基金supported by the Science and Technology Project of China Huaneng Group Co.,Ltd.Research on Key Technologies for Monitoring and Protection of Offshore Wind Power Underwater Equipment(HNKJ21-H40).
文摘As the core facility of offshore wind power systems,the structural safety of offshore booster stations directly impacts the stable operation of entire wind farms.With the global energy transition toward green and lowcarbon goals,offshore wind power has emerged as a key renewable energy source,yet its booster stations face harsh marine environments,including persistent wave impacts,salt spray corrosion,and equipment-induced vibrations.Traditional monitoring methods relying on manual inspections and single-dimensional sensors suffer from critical limitations:low efficiency,poor real-time performance,and inability to capture millinewton-level stress fluctuations that signal early structural fatigue.To address these challenges,this study proposes a biomechanics-driven structural safety monitoring system integrated with deep learning.Inspired by biological stress-sensing mechanisms,the system deploys a distributedmulti-dimensional force sensor network to capture real-time stress distributions in key structural components.A hybrid convolutional neural network-radial basis function(CNN-RBF)model is developed:the CNN branch extracts spatiotemporal features from multi-source sensing data,while the RBF branch reconstructs the nonlinear stress field for accurate anomaly diagnosis.The three-tier architectural design—data layer(distributed sensor array),function layer(CNN-RBF modeling),and application layer(edge computing terminal)—enables a closedloop process from high-resolution data collection to real-time early warning,with data processing delay controlled within 200 ms.Experimental validation against traditional SOM-based systems demonstrates significant performance improvements:monitoring accuracy increased by 19.8%,efficiency by 23.4%,recall rate by 20.5%,and F1 score by 21.6%.Under extreme weather(e.g.,typhoons and winter storms),the system’s stability is 40% higher,with user satisfaction improving by 17.2%.The biomechanics-inspired sensor design enhances survival rates in salt fog(85.7%improvement)and dynamic loads,highlighting its robust engineering applicability for intelligent offshore wind farm maintenance.
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The National High Technology Research and Development Program of China(863Program)(No.2006AA04Z416)
文摘An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.
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funded by the Research and Development Fund Project of China Academy of Railway Science Group Co.,Ltd.,(No:2023YJ259)the Science and Technology Research and Development Program Project of China State Railway Group Co.,Ltd.(No:J2024G008).
文摘Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes,thereby achieving graded post-earthquake response measures.Design/methodology/approach–The seismic intensity monitoring system for railways adopts a two-level architecture,namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform.The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm.A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.Findings–The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang.During the operational period,the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times.Among these,earthquakes triggering the Kriging interpolation algorithm occurred twice.The system operated stably throughout the application period and successfully visualized relevant seismic impact data,such as earthquake intensity distribution maps and affected railway mileage sections.These results validate the system’s practicality and effectiveness.Originality/value–The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network.It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope,thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality.The system can provide clear graded interval recommendations for post-earthquake disposal,effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.
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.
基金supported by the National Research Foundation of Korea(NRF)grants funded by the Ministry of Science,ICT and Future Planning(MSIT)(RS-2024-00408989,RS-2023-00278906,and RS-2023-00217661)the Center for Universitywide Research Facilities(CURF)at Jeonbuk National University for High-Resolution In Vivo Micro-Computed Tomography(Skyscan 1276,BRUKER).
文摘A wearable health monitoring system is a promising device for opening the era of the fourth industrial revolution due to increasing interest in health among modern people.Wearable health monitoring systems were demonstrated by several researchers,but still have critical issues of low performance,inefficient and complex fabrication processes.Here,we present the world’s first wearable multifunctional health monitoring system based on flash-induced porous graphene(FPG).FPG was efficiently synthesized via flash lamp,resulting in a large area in four milliseconds.Moreover,to demonstrate the sensing performance of FPG,a wearable multifunctional health monitoring system was fabricated onto a single substrate.A carbon nanotube-polydimethylsiloxane(CNT-PDMS)nanocomposite electrode was successfully formed on the uneven FPG surface using screen printing.The performance of the FPG-based wearable multifunctional health monitoring system was enhanced by the large surface area of the 3D-porous structure FPG.Finally,the FPG-based wearable multifunctional health monitoring system effectively detected motion,skin temperature,and sweat with a strain GF of 2564.38,a linear thermal response of 0.98Ω℃^(-1) under the skin temperature range,and a low ion detection limit of 10μM.
基金financially supported by the National Natural Science Foundation of China(Nos.62275244,62375258,62225507,U2033211,62175230,and 62175232)the CAS Project for Young Scientists in Basic Research(No.YSBR-065)+2 种基金Scientific Instrument Developing Project of the Chinese Academy of Sciences(No.YJKYYQ20200001)National Key R&D Program of China(No.2022YFB3607800,No.2022YFB3605800,and No.2022YFB4601501)Key Program of the Chinese Academy of Sciences(ZDBS-ZRKJZ-TLC018)。
文摘The intrusion of obstacles onto railway tracks presents a significant threat to train safety,characterized by sudden and unpredictable occurrences.With China leading the world in high-speed rail mileage,ensuring railway security is paramount.The current laser monitoring technologies suffer from high false alarm rates and unreliable intrusion identification.This study addresses these issues by investigating high-resolution laser monitoring technology for railway obstacles,focusing on key parameters such as monitoring range and resolution.We propose an enhanced non-uniform laser scanning method,developing a laser monitoring system that reduces the obstacle false alarm rate to 2.00%,significantly lower than the 20%standard(TJ/GW135-2015).This rate is the best record for laser monitoring systems on China Railway.Our system operates seamlessly in all weather conditions,providing superior accuracy,resolution,and identification efficiency.It is the only 3D LiDAR system certified by the China State Railway Group Co.,Ltd.(Certificate No.[2023]008).Over three years,our system has been deployed at numerous points along various lines managed by the China State Railway Group,accumulating a dataset of 300,000 observations.This extensive deployment has significantly enhanced railway safety.The development and implementation of our railway laser monitoring system represent a substantial advancement in railway safety technology.Its low false alarm rate(2.00%),high accuracy(20 cm×20 cm×20 cm),and robust performance in diverse conditions underscore its potential for widespread adoption,promising to enhance railway safety in China and internationally.
基金supported by Open Research Fund of the Sichuan Institute of Xiamen University(Grant No.202401ZDB004)。
文摘Difficulties in the geometric and performance control of wire laser additive manufacturing have hindered its widespread application.In this study,an in situ process monitoring system that combines a machine vision-based interlayer height controller(IHC)and P-controller-based melt pool temperature controller(MTC)was developed to improve the vertical dimensional accuracy and mechanical properties of off-axis fine-wire laser-directed energy deposition(OAFW-LDED)for 316 L thin-walled parts.The IHC effectively mitigates external defect inheritance,while its synergy with the MTC ensures process stability,improving the vertical dimensional accuracy to±0.2 mm.Grain refinement was achieved by controlling the thermal input to optimize the thermal history and heat accumulation.A heterogeneous microstructure with alternating coarse and fine grains was observed and intergranular thermal cracking was suppressed.The yield and tensile strengths increased from 262 to 416 MPa to 313 and 516 MPa,respectively,with improved consistency in the yield strength between the top and bottom sections.However,excessive laser heat input caused interlayer cracks.Conversely,increasing the heat input through substrate preheating did not induce additional cracks and improved the overall hardness consistency of the thin-walled samples.Therefore,this study proposes a new formability control strategy for OAFW-LDEDs of thin-walled parts.
文摘Protected horticulture makes use of related facilities, engineering technolo- gy and management technologies to create or improve local environment in order to provide optimal environment concerning controllable temperature, humidity, and light for farming and breeding industry, as well as product storage. Protected horticulture is independent to some extent, instead of relying greatly on nature, targeting full use of soil, climate and biological potential. The research concluded production characteristics of protected horticulture and analyzed the application of protected hor- ticulture intelligent monitoring system in protected greenhouse cultivation. In addition, the future development was proposed on protected horticulture intelligent monitoring system.
基金supported by the Department of Electrical Engineering at the National Chin-Yi University of Technology.
文摘Indonesia is a producer in the fisheries sector,with production reaching 14.8 million tons in 2022.The production potential of the fisheries sector can be optimally optimized through aquaculture management.One of the most important issues in aquaculture management is how to efficiently control the fish pond water conditions.IoT technology can be applied to support a fish pond aquaculture monitoring system,especially for catfish species(Siluriformes),in real-time and remotely.One of the technologies that can provide this convenience is the IoT.The problem of this study is how to integrate IoT devices with Firebase’s cloud data system to provide reliable and precise data,which makes it easy for fish cultivators to monitor fishpond conditions in real time and remotely.The IoT aquaculture fishpond monitoring use 3 parameters:(1)water temperature;(2)pHwater level;and(3)turbidity level of pond water.IoT devices use temperature sensors,pH sensors,and turbidity sensors,which are integrated with a microcontroller and Wi-Fi module.Data from sensor readings are sent to the Firebase cloud via theWi-Fi module so that it can be accessed in real time by end users with an Androidbased mobile app.The findings are(1)the IoT-based aquaculture monitoring system device has a low error rate in measuring temprature,pH,and turbidity with a percentage of 1.75%,1.94% and 9.78%,respectively.Overall,the total average error of the three components is 4.49%;(2)in cost analysis,IoT-based has a cost-effectiveness of 94.21% compared to labor costs.An IoT-based aquaculture monitoring system using Firebase can be effectively used as a technology for monitoring fish pond conditions in real-time and remotely for fish cultivators that contribute to providing an IoT-based aquaculture monitoring system that produces valid data,is precise,is easy to implement,and is a low-cost system.
基金Supported by the National Training Program of Innovation and Entrepreneurship for Undergraduates(201310434025)the Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province(BS2013NY004)+2 种基金the Innovation Fund Designated for Post-Doctor of Shandong Province(201302023)the Big Agricultural Data Project of Shandong Agricultural University(75005)the Innovation Fund for Youths of Shandong Agricultural University(23813)~~
文摘Based on the object-oriented concept,the hyperspectral intelligent monitoring system of major soil nutrients was designed and developed by using C# and ArcGIS Engine in combination with Microsoft SQL Server.The system mainly includes the following functions：file operation,basic map operation,spectral preprocessing,model management,nutrient content quick calculation,spatial distribution analysis,user management and so on.This system can accomplish the input and preprocessing of soil hyperspectra,and calculate the content of major nutrients,such as soil organic matter,nitrogen,phosphorus as well as potassium quickly and intelligently based on hyperspectral data.Thereby,the soil nutrients regional distribution in the research area can be analyzed by using the principle of geostatistics.This study can not only promote the practicability of soil quantitative remote sensing,but also provide references for the decision-making of agricultural fertilizing.
基金Supported by the Fundamental Public Welfare Research Program of Zhejiang Provincial Natural Science Foundation,China(LGN18C140007 and Y20C140024)the National High Technology Research and Development Program of China(863 Program,2013AA102402)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.
基金This project was supported by the foundation of the Visual and Auditory Information Processing Laboratory of BeijingUniversity of China (0306) and the National Science Foundation of China (60374031).
文摘Moving object detection is one of the challenging problems in video monitoring systems, especially when the illumination changes and shadow exists. Amethod for real-time moving object detection is described. Anew background model is proposed to handle the illumination varition problem. With optical flow technology and background subtraction, a moving object is extracted quickly and accurately. An effective shadow elimination algorithm based on color features is used to refine the moving obj ects. Experimental results demonstrate that the proposed method can update the background exactly and quickly along with the varition of illumination, and the shadow can be eliminated effectively. The proposed algorithm is a real-time one which the foundation for further object recognition and understanding of video mum＇toting systems.
基金supported by the National Natural Science Foundation (No. 40801225)the Natural Science Foundation of Zhejiang Province (No. LY13D 010005)Young academic leader climbing program of Zhejiang Province (grant number pd 2013222)
文摘The original purpose of Vessel Monitoring System(VMS) is for enforcement and control of vessel sailing. With the application of VMS in fishing vessels, more and more population dynamic studies have used VMS data to improve the accuracy of fisheries stock assessment. In this paper, we simulated the trawl trajectory under different time intervals using the cubic Hermite spline(c Hs) interpolation method based on the VMS data of 8 single otter trawl vessels(totally 36000 data items) fishing in Zhoushan fishing ground from September 2012 to December 2012, and selected the appropriate time interval. We then determined vessels' activities(fishing or non-fishing) by comparing VMS speed data with the corresponding speeds from logbooks. The results showed that the error of simulated trajectory greatly increased with the increase of time intervals of VMS data when they were longer than 30 minutes. Comparing the speeds from VMS with those from the corresponding logbooks, we found that the vessels' speeds were between 2.5 kn and 5.0 kn in fishing. The c Hs interpolation method is a new choice for improving the accuracy of estimation of sailing trajectory, and the VMS can be used to determine the vessels' activities with the analysis of their trajectories and speeds. Therefore, when the fishery information is limited, VMS can be one of the important data sources for fisheries stock assessment, and more attention should be paid to its construction and application to fisheries stock assessment and management.
