Application and Development of Protected Horticulture Intelligent Monitoring System

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.

Hyperspectral Intelligent Monitoring System of Major Soil Nutrients Based on ArcGIS Engine

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.

Data-driven intelligent monitoring system for key variables in wastewater treatment process

In wastewater treatment process(WWTP), the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However, most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue, a data-driven intelligent monitoring system, using the soft sensor technique and data distribution service, is developed to monitor the concentrations of effluent total phosphorous(TP) and ammonia nitrogen(NH_4-N). In this intelligent monitoring system, a fuzzy neural network(FNN) is applied for designing the soft sensor model, and a principal component analysis(PCA) method is used to select the input variables of the soft sensor model. Moreover, data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition(SCADA) system. Finally, this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aeroengine.Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing,an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper.Through analyzing the weakness of convex sparse model,i.e.the tradeoff between noise reduction and feature reconstruction,this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction.Accordingly,a sparsity-assisted deep convolutional variational autoencoders network is proposed,which achieves the intelligent identification of fault state through training denoised normal data.Finally,the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment.The comparison results show that the proposed method is good at abnormal pattern recognition,showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.

Self-Powered,Long-Durable,and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring

Triboelectric nanogenerators(TENGs)have potential to achieve energy harvesting and condition monitoring of oils,the“lifeblood”of industry.However,oil absorption on the solid surfaces is a great challenge for oil-solid TENG(O-TENG).Here,oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.The designed O-TENG can generate an excellent electricity(with a charge density of 9.1μC m^(−2) and a power density of 1.23 mW m^(−2)),which is an order of magnitude higher than other O-TENGs made from polytetrafluoroethylene and polyimide.It also has a significant durability(30,000 cycles)and can power a digital thermometer for self-powered sensor applications.Further,a superhigh-sensitivity O-TENG monitoring system is successfully developed for real-time detecting particle/water contaminants in oils.The O-TENG can detect particle contaminants at least down to 0.01 wt%and water contaminants down to 100 ppm,which are much better than previous online monitoring methods(particle>0.1 wt%;water>1000 ppm).More interesting,the developed O-TENG can also distinguish water from other contaminants,which means the developed O-TENG has a highly water-selective performance.This work provides an ideal strategy for enhancing the output and durability of TENGs for oil-solid contact and opens new intelligent pathways for oil-solid energy harvesting and oil condition monitoring.

Design and application of electrical fire monitoring system in mining industry

To protect mining areas from electrical fire, it is very important to install electrical nre momtormg system to ensure safety in development of mineral resources and for buildings. In this paper, design for electrical fire monitoring and detection system with optional sensor modules has been proposed. In addition, necessity and suitability of electrical fire monitoring and detection system with optional sensor modules in mining areas have been reviewed. The designed electrical fire monitoring and detection system suit- able for work environment of mining industry is composed by host-computer monitoring software and slave-computer detectors. Monitoring detectors are manufactured by using embedded technology. Exter- nal shells deployed have superior enclosure performances and explosion-proof properties. It is easy to install and maintain the system. In general, the system has reached, or even exceeded standards specified in national standards for performances and appearances of such devices. Test results show application of electrical fire monitoring and detection system can effectively enhance monitoring intensity over the mining areas and provide reliable guarantee to ensure orderly development of mineral resources and to protect physical and property safety of citizens in these areas.

Progress of Intelligent Monitoring Technology for Wheat Fusarium Head Blight

Fusarium head blight is one of the most important diseases affecting wheat yield and quality.It is of great significance to carry out intelligent monitoring of wheat Fusarium head blight for high yield,high quality and sustainable development of wheat.On the basis of identifying the harms of wheat Fusarium head blight,this paper analyzed the monitoring technology of wheat Fusarium head blight based on satellite remote sensing,hyperspectral,near-infrared,Internet of things and photoelectric system,to provide a reference for the intelligent monitoring of wheat Fusarium head blight.

A Preliminary Study on Application Effect of Intelligent Insect Sexual Attraction Monitoring System

[Objectives]The paper was to verify and explore the application effect of intelligent insect sexual attraction monitoring system.[Methods]The data of Helicoverpa armigera and Spodoptera frugiperda monitored by intelligent insect sexual attraction monitoring system,manual survey and traditional pest monitoring tool were compared and analyzed,and the application effect of guiding field pest control was investigated.[Results]The statistical data of intelligent insect sexual attraction monitoring system were highly consistent with that of manual survey,and were consistent with that of traditional pest monitoring tool,which had good effect in guiding field control.[Conclusions]The monitoring data of intelligent insect sexual attraction monitoring system are accurate,efficient,real-time and practical.It can solve the problem of high monitoring intensity for the monitoring personnel and conform to the development direction of modern agriculture.

Tool wear condition monitoring method of five-axis machining center based on PSO-CNN

The effective monitoring of tool wear status in the milling process of a five-axis machining center is important for improving product quality and efficiency,so this paper proposes a CNN convolutional neural network model based on the optimization of PSO algorithm to monitor the tool wear status.Firstly,the cutting vibration signals and spindle current signals during the milling process of the five-axis machining center are collected using sensor technology,and the features related to the tool wear status are extracted in the time domain,frequency domain and time-frequency domain to form a feature sample matrix;secondly,the tool wear values corresponding to the above features are measured using an electron microscope and classified into three types:slight wear,normal wear and sharp wear to construct a target Finally,the tool wear sample data set is constructed by using multi-source information fusion technology and input to PSO-CNN model to complete the prediction of tool wear status.The results show that the proposed method can effectively predict the tool wear state with an accuracy of 98.27%;and compared with BP model,CNN model and SVM model,the accuracy indexes are improved by 9.48%,3.44%and 1.72%respectively,which indicates that the PSO-CNN model proposed in this paper has obvious advantages in the field of tool wear state identification.

Research Progress and Prospect of Intelligent Tea Garden Technology Application

The development and application of internet plus modern tea industry technology is more and more extensive.As an important part of the development process of tea industry,intelligent tea garden plays an important role in the development of the whole industry.At present,intelligent tea garden technology is widely used in many fields such as intelligent monitoring,water and fertilizer integration,green prevention and control,quality and safety traceability.In this paper,the application of intelligent tea garden technology in tea gardens was reviewed.On this basis,the development trend of new information technology and tea industry was prospected,in order to provide some reference and thinking for the innovative research of new technology in tea garden in the future.

Value Function Mechanism in WSNs-Based Mango Plantation Monitoring System

Mango fruit is one of the main fruit commodities that contributes to Taiwan’s income.The implementation of technology is an alternative to increasing the quality and quantity of mango plantation product productivity.In this study,a Wireless Sensor Networks(“WSNs”)-based intelligent mango plantation monitoring system will be developed that implements deep reinforcement learning(DRL)technology in carrying out prediction tasks based on three classifications:“optimal,”“sub-optimal,”or“not-optimal”conditions based on three parameters including humidity,temperature,and soil moisture.The key idea is how to provide a precise decision-making mechanism in the real-time monitoring system.A value function-based will be employed to perform DRL model called deep Q-network(DQN)which contributes in optimizing the future reward and performing the precise decision recommendation to the agent and system behavior.The WSNs experiment result indicates the system’s accuracy by capturing the real-time environment parameters is 98.39%.Meanwhile,the results of comparative accuracy model experiments of the proposed DQN,individual Q-learning,uniform coverage(UC),and NaÏe Bayes classifier(NBC)are 97.60%,95.30%,96.50%,and 92.30%,respectively.From the results of the comparative experiment,it can be seen that the proposed DQN used in the study has themost optimal accuracy.Testing with 22 test scenarios for“optimal,”“sub-optimal,”and“not-optimal”conditions was carried out to ensure the system runs well in the real-world data.The accuracy percentage which is generated from the real-world data reaches 95.45%.Fromthe resultsof the cost analysis,the systemcanprovide a low-cost systemcomparedtothe conventional system.

High Output Performance and Ultra-Durable DC Output for Triboelectric Nanogenerator Inspired by Primary Cell

Triboelectric nanogenerator(TENG)is regarded as an effective strategy to convert environment mechanical energy into electricity to meet the distributed energy demand of large number of sensors in the Internet of Things(IoTs).Although TENG based on the coupling of triboelectrification and air-breakdown achieves a large direct current(DC)output,material abrasion is a bottleneck for its applications.Here,inspired by primary cell and its DC signal output characteristics,we propose a novel primary cell structure TENG(PC-TENG)based on contact electrification and electrostatic induction,which has multiple working modes,including contact separation mode,freestanding mode and rotation mode.The PC-TENG produces DC output and operates at low surface contact force.It has an ideal effective charge density(1.02 m Cm^(-2)).Meanwhile,the PC-TENG shows a superior durability with 99% initial output after 100,000 operating cycles.Due to its excellent output performance and durability,a variety of commercial electronic devices are powered by PC-TENG via harvesting wind energy.This work offers a facile and ideal scheme for enhancing the electrical output performance of DC-TENG at low surface contact force and shows a great potential for the energy harvesting applications in IoTs.

Analysis of the Effect and Solution of Geomagnetic Room Humidity on Geomagnetic Observation

It is not easy to control humidity in a geomagnetic room. If humidity is too high or the change is too fast it will lead to an abnormal change on data. The intelligent real-time humidity analysis and monitoring system of a geomagnetic room and probe can not only monitor and display the change of humidity in the geomagnetic room and send an alarm signal when it exceeds the pre-set range, but also dehumidify intelligently. One can arbitrarily control the sensor to monitor the ambient humidity of the probe in order to ensure that the data is stable and true. The design idea and main functions of the system are introduced in the paper.

An Investigative Study for Smart Home Security: Issues, Challenges and Countermeasures

Home security should be a top concern for everyone who owns or rents a home. Moreover, safe and secure residential space is the necessity of every individual as most of the family members are working. The home is left unattended for most of the day-time and home invasion crimes are at its peak as constantly monitoring of the home is difficult. Another reason for the need of home safety is specifically when the elderly person is alone or the kids are with baby-sitter and servant. Home security system i.e. HomeOS is thus applicable and desirable for resident’s safety and convenience. This will be achieved by turning your home into a smart home by intelligent remote monitoring. Smart home comes into picture for the purpose of controlling and monitoring the home. It will give you peace of mind, as you can have a close watch and stay connected anytime, anywhere. But, is common man really concerned about home security? An investigative study was done by conducting a survey to get the inputs from different people from diverse backgrounds. The main motivation behind this survey was to make people aware of advanced HomeOS and analyze their need for security. This paper also studied the necessity of HomeOS investigative study in current situation where the home burglaries are rising at an exponential rate. In order to arrive at findings and conclusions, data were analyzed. The graphical method was employed to identify the relative significance of home security. From this analysis, we can infer that the cases of having kids and aged person at home or location of home contribute significantly to the need of advanced home security system. At the end, the proposed system model with its flow and the challenges faced while implementing home security systems are also discussed.

An Automated Detection Approach of Protective Equipment Donning for Medical Staff under COVID-19 Using Deep Learning

Personal protective equipment(PPE)donning detection for medical staff is a key link of medical operation safety guarantee and is of great significance to combat COVID-19.However,the lack of dedicated datasets makes the scarce research on intelligence monitoring of workers’PPE use in the field of healthcare.In this paper,we construct a dress codes dataset for medical staff under the epidemic.And based on this,we propose a PPE donning automatic detection approach using deep learning.With the participation of health care personnel,we organize 6 volunteers dressed in different combinations of PPE to simulate more dress situations in the preset structured environment,and an effective and robust dataset is constructed with a total of 5233 preprocessed images.Starting from the task’s dual requirements for speed and accuracy,we use the YOLOv4 convolutional neural network as our learning model to judge whether the donning of different PPE classes corresponds to the body parts of the medical staff meets the dress codes to ensure their self-protection safety.Experimental results show that compared with three typical deeplearning-based detection models,our method achieves a relatively optimal balance while ensuring high detection accuracy(84.14%),with faster processing time(42.02 ms)after the average analysis of 17 classes of PPE donning situation.Overall,this research focuses on the automatic detection of worker safety protection for the first time in healthcare,which will help to improve its technical level of risk management and the ability to respond to potentially hazardous events.

Digital twin-enabled smart facility management: A bibliometric review

In recent years,the architecture,engineering,construction,and facility management(FM)industries have been applying various emerging digital technologies to facilitate the design,construction,and management of infrastructure facilities.Digital twin(DT)has emerged as a solution for enabling real-time data acquisition,transfer,analysis,and utilization for improved decision-making toward smart FM.Substantial research on DT for FM has been undertaken in the past decade.This paper presents a bibliometric analysis of the literature on DT for FM.A total of 248 research articles are obtained from the Scopus and Web of Science databases.VOSviewer is then utilized to conduct bibliometric analysis and visualize keyword co-occurrence,citation,and co-authorship networks;furthermore,the research topics,authors,sources,and countries contributing to the use of DT for FM are identified.The findings show that the current research of DT in FM focuses on building information modeling-based FM,artificial intelligence(AI)-based predictive maintenance,real-time cyber–physical system data integration,and facility lifecycle asset management.Several areas,such as AI-based real-time asset prognostics and health management,virtual-based intelligent infrastructure monitoring,deep learning-aided continuous improvement of the FM systems,semantically rich data interoperability throughout the facility lifecycle,and autonomous control feedback,need to be further studied.This review contributes to the body of knowledge on digital transformation and smart FM by identifying the landscape,state-of-the-art research trends,and future needs with regard to DT in FM.

Intelligent monitoring method of cow ruminant behavior based on video analysis technology

To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study.The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately.The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video.In this way,the monitoring of the ruminant behavior of dairy cows was realized.To verify the accuracy of the method,six videos,a total of 99'00",24000 frames were selected.The test results demonstrated that the success rate of this method was 92.03%,despite the interference of behaviors,such as raising or turning of the cow’s head.The results demonstrate that this method,which monitors the ruminant behavior of dairy cows,is effective and feasible.

The Intelligent Door Monitoring System on Internet

The hardware structure and software function of intelligence door monitoring control systems on Internet is expounded. The remote managing function, database function, and the realization of dialing users are introduced. The reset card is installed to improve reliability. The design of the system is reasonable and reliable. Results showed that 10 percent of the line investment have been cut off.

Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Automatic monitoring of cow rumination has great significance in the development of modern animal husbandry.In order to solve the problem of high real-time requirement of ruminant behavior monitoring,a tracking method based on STC(Spatio-Temporal Context)learning was carried out.On the basis of cow’s mouth region extraction,the spatial context model between target object and its local surrounding background was built based on their spatial correlations by solving the deconvolution problem,and the learned spatial context model was used to update the STC learning model for the next frame.Tracking in the next frame was formulated by computing a confidence map as a convolution problem that integrates the STC learning information,and the best object location could be estimated by maximizing the confidence map.Then the target scale was estimated based on the confidence evaluation.Finally,accurate tracking of the mouth movement trajectory was realized.To verify the effectiveness of the proposed method,the performance of the algorithm was tested using 20 video sequences.Besides,the tracking results were compared with the Mean-shift algorithm.The results showed that the average success rate of STC learning monitoring algorithm was 85.45%,which was 9.45%higher than the Mean-shift algorithm,the detection rate of STC learning monitoring algorithm was 18.56 s per video,which was 22.08%higher than that of the Mean-shift algorithm.The results showed that the fast tracking method based on STC learning monitoring algorithm is effective and feasible.

Intelligent Tool Failure Monitoring for Machining Processes

An improv6d strategy is Presented for intelligent tool weer monltoring under varying cutting conditions.The proposed strategy uses wear feature extraction based on process modelling and parameter estimation. Theadaptive model traces the properties of cutting processes by combining process state signals,cutting conditions, aforce model and the least squares method. The tool wear feature is obtained the estimated parameters of themodel. Experimental results show that changes of the peraoders in the cutting force model reliably indicate toolwear independent of variation of the cutting conditions.