Design of Intelligent Window Automatic Monitoring System Based on Microcontroller Control

To ensure the safety of residents’lives and property by using automatic opening and closing of ordinary windows,this article designs an intelligent window automatic monitoring system.The article proposes a software and hardware design scheme for the system,which comprises a microcontroller control module,temperature and humidity detection module,harmful gas detection module,rainfall detection module,human thermal radiation induction module,Organic Light-Emitting Diode(OLED)display module,stepper motor drive module,Wi-Fi communication module,etc.Users use this system to monitor environmental data such as temperature,humidity,rainfall,harmful gas concentrations,and human health.Users can control the opening and closing of windows through manual,microcontroller,and mobile application(app)remote methods,providing users with a more convenient,comfortable,and safe living environment.

IoT-Enabled Plant Monitoring System with Power Optimization and Secure Authentication

Global food security is a pressing issue that affects the stability and well-being of communities worldwide.While existing Internet of Things(IoT)enabled plant monitoring systems have made significant strides in agricultural monitoring,they often face limitations such as high power consumption,restricted mobility,complex deployment requirements,and inadequate security measures for data access.This paper introduces an enhanced IoT application for agricultural monitoring systems that address these critical shortcomings.Our system strategically combines power efficiency,portability,and secure access capabilities,assisting farmers in monitoring and tracking crop environmental conditions.The proposed system includes a remote camera that captures images of surrounding plants and a sensor module that regularly monitors various environmental factors,including temperature,humidity,and soil moisture.We implement power management strategies to minimize energy consumption compared to existing solutions.Unlike conventional systems,our implementation utilizes the Amazon Web Services(AWS)cloud platform for reliable data storage and processing while incorporating comprehensive security measures,including Two-Factor Authentication(2FA)and JSON Web Tokens(JWT),features often overlooked in current agricultural IoT solutions.Users can access this secure monitoring system via a developed Android application,providing convenient mobile access to the gathered plant data.We validate our system’s advantages by implementing it with two potted garlic plants on Okayama University’s rooftop.Our evaluation demonstrates high sensor reliabil-ity,with strong correlations between sensor readings and reference data,achieving determination coefficients(R2)of 0.979 for temperature and 0.750 for humidity measurements.The implemented power management strategies extend battery life to 10 days on a single charge,significantly outperforming existing systems that typically require daily recharging.Furthermore,our dual-layer security implementation utilizing 2FA and JWT successfully protects sensitive agricultural data from unauthorized access.

Design and Construction of Automatic Monitoring System for Open-pit Coal Mine Slopes

[Objectives]To monitor the stability of open-pit coal mine slopes in real time and ensure the safety of coal mine production.[Methods]The automatic monitoring system of coal mine slope was explored in depth,and the core functions of the system were designed comprehensively.According to the design function of the automatic monitoring system,the slope automatic monitoring system was constructed.Besides,in accordance with the actual situation of the slope,the monitoring frequency of slopes was set scientifically,and the key indicators such as rainfall,deep displacement and surface displacement of the slopes were monitored in an all-round and multi-angle way.[Results]During the monitoring period,the overall condition of the slope remained good,and no landslides or other geological disasters occurred.At the same time,the overall rainfall in the slope area remained low.In terms of monitoring data,the horizontal displacement and settlement of the slopes increased first and then tended to be stable.Specifically,the maximum horizontal displacement during the monitoring period was 22.74 mm,while the maximum settlement was 18.65 mm.[Conclusions]The automatic slope monitoring system has obtained remarkable achievements in practical application.It not only improves the accuracy and efficiency of slope stability monitoring,but also provides valuable reference experience for similar projects.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Fault diagnosis method of link control system for gravitational wave detection

To maintain the stability of the inter-satellite link for gravitational wave detection,an intelligent learning monitoring and fast warning method of the inter-satellite link control system failure is proposed.Different from the traditional fault diagnosis optimization algorithms,the fault intelligent learning method pro-posed in this paper is able to quickly identify the faults of inter-satellite link control system despite the existence of strong cou-pling nonlinearity.By constructing a two-layer learning network,the method enables efficient joint diagnosis of fault areas and fault parameters.The simulation results show that the average identification time of the system fault area and fault parameters is 0.27 s,and the fault diagnosis efficiency is improved by 99.8%compared with the traditional algorithm.

Research on the monitoring system for induced voltage and ground current of 27.5 kV cable sheath in railways

Purpose–The purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution.Design/methodology/approach–Through establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors,the principle of grounding current monitoring is proposed.Furthermore,the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments.Finally,through practical application in the traction substation of the railway bureau on site,a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments.Findings–The experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status.The system performs excellently in terms of data collection accuracy,real-time performance and reliability of alarm functions.In addition,the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications,providing strong technical support for the safe operation of highspeed railway traction power supply systems.Originality/value–This study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system,which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current.The design,experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance,contributing innovative solutions to the field of railway power supply safety monitoring.

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.

Utilising continuous glucose monitoring for glycemic control in diabetic kidney disease

In this editorial,we comment on the article by Zhang et al.Chronic kidney disease(CKD)presents a significant challenge in managing glycemic control,especially in diabetic patients with diabetic kidney disease undergoing dialysis or kidney transplantation.Conventional markers like glycated haemoglobin(HbA1c)may not accurately reflect glycemic fluctuations in these populations due to factors such as anaemia and kidney dysfunction.This comprehensive review discusses the limitations of HbA1c and explores alternative methods,such as continuous glucose monitoring(CGM)in CKD patients.CGM emerges as a promising technology offering real-time or retrospective glucose concentration measure-ments and overcoming the limitations of HbA1c.Key studies demonstrate the utility of CGM in different CKD settings,including hemodialysis and peritoneal dialysis patients,as well as kidney transplant recipients.Despite challenges like sensor accuracy fluctuation,CGM proves valuable in monitoring glycemic trends and mitigating the risk of hypo-and hyperglycemia,to which CKD patients are prone.The review also addresses the limitations of CGM in CKD patients,emphasizing the need for further research to optimize its utilization in clinical practice.Altogether,this review advocates for integrating CGM into managing glycemia in CKD patients,highlighting its superiority over traditional markers and urging clinicians to consider CGM a valuable tool in their armamentarium.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Personalized Health Monitoring Systems: Integrating Wearable and AI

The integration of wearable technologies and artificial intelligence (AI) has revolutionized healthcare, enabling advanced personal health monitoring systems. This article explores the transformative impact of wearable technologies and AI on healthcare, highlighting the development and theoretical application of the Integrated Personal Health Monitoring System (IPHMS). By integrating data from various wearable devices, such as smartphones, Apple Watches, and Oura Rings, the IPHMS framework aims to revolutionize personal health monitoring through real-time alerts, comprehensive tracking, and personalized insights. Despite its potential, the practical implementation faces challenges, including data privacy, system interoperability, and scalability. The evolution of healthcare technology from traditional methods to AI-enhanced wearables underscores a significant advancement towards personalized care, necessitating further research and innovation to address existing limitations and fully realize the benefits of such integrated health monitoring systems.

Long-term trends in the abundance and breeding performance in Adélie penguins:the Argentine Ecosystem Monitoring Program

In this work,we report long-term trends in the abundance and breeding performance of Adélie penguins(Pygoscelis adeliae)nesting in three Antarctic colonies(i.e.,at Martin Point,South Orkneys Islands;Stranger Point/Cabo Funes,South Shetland Islands;and Esperanza/Hope Bay in the Antarctic Peninsula)from 1995/96 to 2022/23.Using yearly count data of breeding groups selected,we observed a decline in the number of breeding pairs and chicks in crèche at all colonies studied.However,the magnitude of change was higher at Stranger Point than that in the remaining colonies.Moreover,the index of breeding success,which was calculated as the ratio of chicks in crèche to breeding pairs,exhibited no apparent trend throughout the study period.However,it displayed greater variability at Martin Point compared to the other two colonies under investigation.Although the number of chicks in crèche of Adélie penguins showed a declining pattern,the average breeding performance was similar to that reported in gentoo penguin colonies,specifically,those undergoing a population increase(even in sympatric colonies facing similar local conditions).Consequently,it is plausible to assume a reduction of the over-winter survival as a likely cause of the declining trend observed,at least in the Stranger Point and Esperanza colonies.However,we cannot rule out local effects during the breeding season affecting the Adélie population of Martin Point.

Research on Smart Energy Monitoring and Management System Based on Digital Twin Technology

Smart energy monitoring and management system lays a foundation for the application and development of smart energy. However, in recent years, the work efficiency of smart energy development enterprises has generally been low, and there is an urgent need to improve the application efficiency, resilience and sustainability of smart energy monitoring and management system. Digital twin technology provides a data-centric solution to improve smart energy monitoring and management system, bringing an opportunity to transform passive infrastructure assets into data-centric systems. This paper expounds on the concept and key technologies of digital twin, and designs a smart energy monitoring and management system based on digital twin technology, which has dual significance for promoting the development of smart energy field and promoting the application of digital twin.

Research on a Comprehensive Monitoring System for Tunnel Operation based on the Internet of Things and Artificial Intelligence Identification Technology

This article proposes a comprehensive monitoring system for tunnel operation to address the risks associated with tunnel operations.These risks include safety control risks,increased traffic flow,extreme weather events,and movement of tectonic plates.The proposed system is based on the Internet of Things and artificial intelligence identification technology.The monitoring system will cover various aspects of tunnel operations,such as the slope of the entrance,the structural safety of the cave body,toxic and harmful gases that may appear during operation,excessively high and low-temperature humidity,poor illumination,water leakage or road water accumulation caused by extreme weather,combustion and smoke caused by fires,and more.The system will enable comprehensive monitoring and early warning of fire protection systems,accident vehicles,and overheating vehicles.This will effectively improve safety during tunnel operation.

Automatic Monitoring System for 3-D Deformation of Crustal Fault Based on Laser and Machine Vision

An automatic monitoring method of the 3-D deformation is presented for crustal fault based on laser and machine vision. The laser source and screen are independently set up in the headwall and footwall, the collimated laser beam creates a circular spot on the screen, meanwhile, the industrial camera captures the tiny deformation of the crustal fault by monitoring the change of the spot position. This method significantly reduces the cost of equipment and labor, provides daily sampling to ensure high continuity of data. A prototype of the automatic monitoring system is developed, and a repeatability test indicates that the error of spot jitter can be minimized by consecutive samples. Meanwhile, the environmental correction model is determined to ensure that environmental changes do not disturb the system. Furthermore, the automatic monitoring system has been applied at the deformation monitoring station(KJX02) of China Beishan underground research laboratory, where continuous deformation monitoring is underway.

Design of IoT-based Volatile Organic Compounds Monitoring System

Volatile organic compounds(VOC)gas detection devices based on semiconductor sensors have become a common method due to their low cost,simple principle,and small size.However,with the continuous development of materials science,various new materials have been applied in the fabrication of gas sensors,but these new materials have more stringent requirements for operating temperature,which cannot be met by existing sensor modules on the market.Therefore,this paper proposes a temperature-adjustable sensor module and designs an environmental monitoring system based on the STM32F103RET6 microprocessor.This system primarily utilizes multiple semiconductor gas sensors to monitor and record the concentrations of various harmful gases in different environments.It can also monitor real-time temperature,humidity,and latitude and longitude in the current environment,and upload the data to the Internet of Things via 4G communication.This system has the advantages of small size,portability,and low cost.Experimental results show that the sensor module can achieve precise control of operating temperature to a certain extent,with an average temperature error of approximately 3%.The monitoring system demonstrates a certain level of accuracy in detecting target gases and can promptly upload the data to a cloud platform for storage and processing.A comparison with professional testing equipment shows that the sensitivity curves of each sensor exhibit similarity.This study provides engineering and technical references for the application of VOC gas sensors.

IoT Based Nurse Activities Monitoring and Controlling System

IoT technology has emerged as a valuable tool in modern healthcare, providing real-time monitoring of patients, effective management of healthcare, and proper administration of patient information. The proposed system aims to develop a system that can prevent backward blood flow from stopping saline fluid, as well as monitor the temperature, heart rate, and oxygen level of patients by using multiple sensors like weight, temperature and heart rate, etc. Additionally, the proposed system can monitor the room temperature and humidity for contributing to the patient’s overall comfort. In emergency situations, it includes an emergency push button for quick alert medical staff and initiates timely interventions. It is designed to support nurses and doctors in monitoring patients and providing timely interventions to prevent complications.

A wireless solution for greenhouse monitoring and control system based on ZigBee technology

With the rapid development of wireless technologies, it is possible for Chinese greenhouses to be equipped with wireless sensor networks due to their low-cost, simplicity and mobility. In the current study, we compared the advantages of ZigBee with other two similar wireless networking protocols, Wi-Fi and Bluetooth, and proposed a wireless solution for green- house monitoring and control system based on ZigBee technology. As an explorative application of ZigBee technology in Chinese greenhouse, it may promote Chinese protected agriculture.

Wearable Smart Sensor System for Medical Monitoring with an Assessment of the Level of Blood Loss and Pain Shock Because of Trauma or Injury

Blood loss in peacetime is mainly due to the normal menstrual cycle in women or diseases with surgical intervention. In wartime, blood loss in military personnel is a characteristic sign of a closed or open injury of the body during internal or external bleeding. Access to clinical care for wounded military personnel injured on the battlefield is limited and has long delays compared to patients in peacetime. Most of the deaths of wounded military personnel on the battlefield occur within the first hour after being wounded. The most common causes are delay in providing medical care, loss of time for diagnosis, delay in stabilization of pain shock and large blood loss. Some help in overcoming these problems is provided by the data in the individual capsule, which each soldier of the modern army possesses;however, data in an individual capsule is not sufficient to provide emergency medical care in field and hospital conditions. This paper considers a project for development of a smart real-time monitoring wearable system for blood loss and level of shock stress in wounded persons on the battlefield, which provides medical staff in field and hospital conditions with the necessary information to give timely medical care. Although the hospital will require additional information, the basic information about the victims will already be known before he enters the hospital. It is important to emphasize that the key term in this approach is monitoring. It is tracking, and not a one-time measurement of indicators, that is crucial in a valid definition of bleeding.

An IoT-Based Aquaculture Monitoring System Using Firebase

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.

Application and management of continuous glucose monitoring in diabetic kidney disease

Diabetic kidney disease(DKD)is a common complication of diabetes mellitus that contributes to the risk of end-stage kidney disease(ESKD).Wide glycemic var-iations,such as hypoglycemia and hyperglycemia,are broadly found in diabetic patients with DKD and especially ESKD,as a result of impaired renal metabolism.It is essential to monitor glycemia for effective management of DKD.Hemoglobin A1c(HbA1c)has long been considered as the gold standard for monitoring glycemia for>3 months.However,assessment of HbA1c has some bias as it is susceptible to factors such as anemia and liver or kidney dysfunction.Continuous glucose monitoring(CGM)has provided new insights on glycemic assessment and management.CGM directly measures glucose level in interstitial fluid,reports real-time or retrospective glucose concentration,and provides multiple glycemic metrics.It avoids the pitfalls of HbA1c in some contexts,and may serve as a precise alternative to estimation of mean glucose and glycemic variability.Emerging studies have demonstrated the merits of CGM for precise monitoring,which allows fine-tuning of glycemic management in diabetic patients.Therefore,CGM technology has the potential for better glycemic monitoring in DKD patients.More research is needed to explore its application and management in different stages of DKD,including hemodialysis,peritoneal dialysis and kidney transplantation.