Monitoring System For Detection Research Articles

КРИЗА ЯК КАТАЛІЗАТОР РОЗВИТКУ БІЗНЕС-ОРГАНІЗАЦІЇ

The article provides an in-depth analysis of the concept of "crisis" in a business context and summarises approaches to defining this concept. Different types of crises are categorised based on their origin and diverse manifestations. "Crisis" is regarded as a phenomenon that can present opportunities and challenges for generating new strategies and adaptations that will help a business organisation to maintain a competitive advantage in the market. Crisis phenomena can contribute to business growth, but only if businesses have learned to adapt to market changes and find new sources of finance. During an economic crisis, business organisations can expand and develop by using new technologies, growing e-commerce, attracting new customers, establishing cooperation and partnership relationships, etc. This research article presents a systematic approach to the available modern crisis management tools and explores strategies for business development during crisis times. A business's response and recovery from a crisis can be greatly facilitated by thorough planning and preparation for potential crisis situations. Crisis management involves the establishment of monitoring systems for the early detection of potential crisis situations. This enables the organisation to take rapid action before the situation escalates. Early detection can significantly reduce the impact and severity of a crisis. Effective crisis management begins with proactive planning. Organisations develop comprehensive crisis management plans that outline potential risks, scenarios and strategies for managing different types of crises. The study showed that it is important to maintain the trust of both customers and employees, look for new opportunities for growth, and act quickly and decisively in difficult circumstances. Business leaders and managers need to be prepared for unforeseen changes, act quickly when the market changes and constantly look for new opportunities for growth in order to deal effectively with crisis situations.

Multiple Fault Detection and Smart Monitoring System Based on Machine Learning Classifiers for Infant Incubators Using Raspberry Pi 4

The most significant and sensitive component of the biomedical field is the care of newborn newborns. Because of their gestational age or birth weight, certain newborn newborns are at a higher risk of death. Because of their unmet demand for warmth, the majority of preterm newborns born between 32 and 37 weeks of gestation die. The neonatal incubator is a device that provides a regulated and closed environment to preterm newborns. In this paper and depending on technological progress, an intelligent system has been designed to monitor the performance of the incubator sensors depending on four features (temperature, humidity, fan current, and heater current) to detect any fault in the system. The intelligent system employs a low-power computing device to detect the fault in sensors, like the Raspberry Pi 4, which delivers the data from the incubator’s sensors. For classification, tasks adopted many algorithms like Decision Tree (DT), Support Vector Machine (SVM), and Neural Network (NN), then send alerting messages (e-mail, text message) to the nurse or doctor via Wi-Fi. The promising results of the proposed method accuracy come as 98% of DT and SVM while 97.3% of NN.

Review of the Monitoring Applications Involved in the Underground Storage of Natural Gas and CO2

Natural gas is an indispensable resource not evenly distributed in the world. The gas supply chain is characterized by large imbalances between supply and demand, where the underground gas storage (UGS) application plays a key role for creating strategic reserves, taking advantage of geological structures. On the contrary, human activities will require clean energy with near-zero greenhouse gas emissions to be environmentally viable. A key element of this strategy is the carbon capture and storage (CCS) application useful for confining CO2 into the geosphere to reduce anthropogenic emissions. The development of appropriate injection methods and long-term monitoring systems for leak detection of the underground storage of natural gas and CO2 is important to prevent negative effects, such as ground deformations and micro seismic events. In this work, a variety of monitoring applications were gathered and critically analyzed for a total of 60 scientific contributions spanning the world. This bibliographic work shows an analytical and statistical overview of the most common use of UGS and CCS, representing the different goals of these two applications and analyzing the main monitoring techniques used in the gathered contributions. Currently, UGS monitoring requires further development, especially through multidisciplinary approaches useful for identifying possible effects on the surface and gas leaks at depth; meanwhile, CCS solutions are still at the experimental stage, also because of the high costs for large-scale applications that still need specific research. The state of the art of these two very different practices can improve the further development of new monitoring approaches or additional methods.

An intelligent fault detection and diagnosis monitoring system for reactor operational resilience: Power transient identification

Various microreactor designs under development aim at fulfilling electricity and heat production requirements affordably and reliably in a variety of applications, like power generation for remote communities and military bases. The fission battery deployment strategy for microreactors provides battery-like simplicity for its installation and operation. A successful fission battery must be cost competitive, have unit standardization, enable flexible installation, possess high reliability, and feature unattended operations. Due to the design and operational features of microreactors, successful unattended operations of the system must be enabled through an on-line monitoring (OLM) system that can effectively and reliably detect and diagnose any fault in the reactor. This research introduces the concept and approach of an intelligent Fault Detection and Diagnosis Monitoring System (FDDMS) that can provide power transient dependent fault detection and diagnosis to fit within a future autonomous control framework. Specifically, this paper develops the first FDDMS module, the Power Transient Module, to reliably identify reactor operations as steady state, ramping up in power, or ramping down in power using data from a broad-scope simulator. Knowledge of the current power transient in the reactor is paramount for effective fault detection and diagnosis in any operational regime. This paper explores various data-driven methods to accurately evaluate the power transients: 1) Principal Component Analysis and Support Vector Machines, 2) Deep Neural Networks, and 3) Convolutional Neural Networks. To prepare the sensor data collected from the simulator for training the data-driven models, several preprocessing techniques were tested to optimize for evaluation accuracy. The final model selected for the Power Transient Module produced perfect evaluation results for several power transients, including in previously unseen regimes. Foundation for further module development of the FDDMS was established. Future works will see the implementation of the remaining FDDMS architecture.

A Novel CNN-LSTM Hybrid Model for Prediction of Electro-Mechanical Impedance Signal Based Bond Strength Monitoring.

The recent application of deep learning for structural health monitoring systems for damage detection has potential for improvised structure performance and maintenance for long term durability, and reliable strength. Advancements in electro-mechanical impedance (EMI) techniques have sparked attention among researchers to develop novel monitoring techniques for structural monitoring and evaluation. This study aims to determine the performance of EMI techniques using a piezo sensor to monitor the development of bond strength in reinforced concrete through a pull-out test. The concrete cylindrical samples with embedded steel bars were prepared, cured for 28 days, and a pull-out test was performed to measure the interfacial bond between them. The piezo coupled signatures were obtained for the PZT patch bonded to the steel bar. The damage qualification is performed through the statistical indices, i.e., root-mean-square deviation (RMSD) and correlation coefficient deviation metric (CCDM), were obtained for different displacements recorded for axial pull. Furthermore, this study utilizes a novel Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)-based hybrid model, an effective regression model to predict the EMI signatures. These results emphasize the efficiency and potential application of the deep learning-based hybrid model in predicting EMI-based structural signatures. The findings of this study have several implications for structural health diagnosis using a deep learning-based model for monitoring and conservation of building heritage.

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

Vibration Analysis for Damage Detection in Composite Plate by using Piezoelectric Sensors

This study focuses on an active monitoring method for damage detection applied to composite structures. Different specimens made up of fiber epoxy resin in the form of composite structures are studied. Wavelet analysis methods are adopted to post process the raw monitored signal. A new damage signature is introduced to determine the presence and extent of damage in composites, while eliminating the influence of different distances between the active actuator and active monitoring elements. The proposed method is shown to be effective, reliable, and straightforward for the specimens considered in the present study, which are composed of different materials and suffer various levels of damage. An online real-time active monitoring system for damage detection is described that is based on this research.

Optimizing national border reopening policies in the COVID-19 pandemic: A modeling study.

After emergence of the COVID-19 pandemic and subsequent restrictions, countries worldwide have sought to reopen as quickly as possible. However, reopening involves the risk of epidemic rebound. In this study, we investigated the effective policy combination to ensure safe reopen. On the basis of the classical SEIR epidemic model, we constructed a COVID-19 system dynamics model, incorporating vaccination, border screening, and fever clinic unit monitoring policies. The case of China was used to validate the model and then to test policy combinations for safe reopening. Vaccination was found to be crucial for safe reopening. When the vaccination rate reached 60%, the daily number of newly confirmed COVID-19 cases began to drop significantly and stabilized around 1,400 [1/1,000,000]. The border screening policy alone only delayed epidemic spread for 8 days but did not reduce the number of infections. Fever clinic unit monitoring alone could reduce the peak of new confirmed cases by 44% when the case identification rate rose from 20 to 80%. When combining polices, once the vaccination rate reached 70%, daily new confirmed cases stabilized at 90 [0.64/1,000,000] with an 80% case identification rate at fever clinic units and border screening. For new variants, newly confirmed cases did not stabilize until the vaccination rate reached 90%. High vaccination rate is the base for reopening. Vaccination passport is less effective compared with a strong primary care monitoring system for early detection and isolation of the infected cases.

Monitoring Sparse and Attributed Network Streams with MultiLevel and Dynamic Structures

In this study, we create a new monitoring system for change detection in sparse attributed network streams with multilevel or nested dynamic structures. To achieve this, we hypothesize that the contingency of establishing an edge between two network nodes at time t depends on the properties of the network edges, network nodes, groups, or categories. Then, we estimate the model parameters using the expressed logit model. The model parameters are developed using the state-space model to achieve a dynamic state in the system. The extended Kalman filter (EKF) updates state-space parameters and predicts upcoming networks. Predicted residuals are tracked using statistical process control charts to identify changes in the underlying mechanism of edge generation. This research makes a methodological contribution by combining zero-inflated generalized linear mixed models (ZI-GLMMs) with the state-space model to monitor changes in the sequences of sparse, attributed, and weighted multilevel networks by applying control charts. The proposed model is compared to previous models to evaluate performance by implementing three scenarios. The results show that the model is faster at detecting the first change. Finally, using real e-MID data, we measured the model’s performance in detecting real data changes. The findings suggest that the proposed model could predict a crisis in advance of significant European Central Bank statements and events.

The use of circumferential guided waves to monitor axial cracks in pipes

Guided wave testing is in routine use to detect corrosion, particularly in the oil and gas sector, but the detection of axial cracking is difficult using the axially propagating waves commonly used for corrosion detection. This paper presents a novel guided wave monitoring technique for the detection of axial cracking in piping. Circumferential SH0 waves, travelling around the circumference were used in a monitoring configuration to detect axial defects in a 6 inch schedule 80 steel pipe. Finite Element (FE) analysis showed that both the defect reflection and the decline in the transmitted SH0 wave can be used for defect detection. Baseline subtraction was utilised to produce residual signals that can be better analysed for small defect reflections. The Root-Mean-Square (RMS) of the residual signal after baseline subtraction was found to be the most satisfactory means of monitoring defect progression. An amplification effect was identified where the residual signal is compounded by continued interaction with the defect on each revolution of the incident wave. The FE predictions were validated by an experiment in which an Electrical Discharge Machining (EDM) notch was grown in four stages. Temperature compensation using Location Specific Temperature Compensation (LSTC) was applied to the experimental data, allowing residuals to be compared for a ∼10 °C temperature swing over a monitoring period of over 2 months. It was determined that a 10 mm long (∼23% of wavelength), 5 mm deep (∼45% of the wall thickness) defect at an axial offset from the line of the transducers of 1.5 wavelengths (65 mm) would be readily detectable with a very high Probability of Detection (POD) and virtually no chance of a false call. Therefore, this novel guided wave monitoring system for axial crack detection is likely to be attractive for applications in a range of industries for its sensitivity to axial cracking combined with large coverage from a single transducer location.

AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System for COVID-19 Prevention

The outbreak of COVID-19 had brought a great challenge for the World Health Organization (WHO) in preventing the spreading of SARS-CoV-2. The Ministry of Health (MOH) of Malaysia introduced the MySejahtera mobile application for health monitoring and contact tracing. Wearing a face mask in public areas had been made compulsory by the Government. The overhead cost incurred in hiring the extra manpower to ensure all the visitors wear a face mask, check-in through MySejahtera and the status in MySejahtera is healthy before entering a premise. A low-cost solution is urgently needed to reduce the heavy overhead cost. An AI-Based Low-Cost Real-Time Face Mask Detection and Health Status Monitoring System (AFMHS) is proposed to perform real-time detection for the face mask and MySejahtera Check-In tickets by using artificial intelligence. MobileNetV2 was used for the detection and recognition of face and face masks. YOLOv3 was used for the detection of the region of interest for the MySejahtera Check-In ticket to locate the health and vaccination status of the visitor. Optical character recognition (OCR) is a technique that is used to detect the text captured in an image and encode the recognized text. OCR is implemented to recognize the text extracted from the ticket. Tesseract is used as the OCR engine in AFMHS. Raspberry-Pi-4B (Raspberry Pi Generation 4 Model B) with 4 GB RAM is used as the processing unit of AFMHS. The total cost of the AFMHS is only USD220. Extensive experimental tests were carried out to evaluate the performance of AFMHS. The optimum operation setup conditions are proposed to achieve 100% accuracy. The optimum operating distance for the face mask detector and MySejahtera Check-In ticket detector are 1.5m and 15cm respectively.

An energy and leakage current monitoring system for abnormality detection in electrical appliances

Unsafe electrical appliances can be hazardous to humans and can cause electrical fires if not monitored, analyzed, and controlled. The purpose of this study is to monitor the system’s condition, including the electrical properties of the appliances, and to diagnose fault conditions without deploying sensors on individual appliances and analyzing individual sensor data. Using historical data and an acceptable range of normal and leakage currents, we proposed a hybrid model based on multiclass support vector machines (MSVM) integrated with a rule-based classifier (RBC) to determine the changes in leakage currents caused by installed devices at a certain moment. For this, we developed a sensor-based monitoring device with long-range communication to store real-time data in a cloud database. In the modeling process, RBC algorithm is used to diagnose the constructed device fault and overcurrent fault where MSVM is applied for detecting leakage current fault. To conduct an operational field test, the developed device was integrated into some houses. The results demonstrate the effectiveness of the proposed system in terms of electrical safety monitoring and detection. All the collected data were stored in a structured database that could be remotely accessed through the Internet.

Point-of-care vertical flow immunoassay system for ultra-sensitive multiplex biothreat-agent detection in biological fluids

This paper presents simple, fast, and sensitive detection of multiple biothreat agents by paper-based vertical flow colorimetric sandwich immunoassay for detection of Yersinia pestis (LcrV and F1) and Francisella tularensis (lipopolysaccharide; LPS) antigens using a vertical flow immunoassay (VFI) prototype with portable syringe pump and a new membrane holder. The capture antibody (cAb) printing onto nitrocellulose membrane and gold-labelled detection antibody (dAb) were optimized to enhance the assay sensitivity and specificity. Even though the paper pore size was relaxed from previous 0.1 μm to the current 0.45 μm for serum samples, detection limits as low as 0.050 ng/mL for LcrV and F1, and 0.100 ng/mL for FtLPS have been achieved in buffer and similarly in diluted serum (with LcrV and F1 LODs remained the same and LPS LOD reduced to 0.250 ng/mL). These were 40, 80, and 50X (20X for LPS in serum) better than those from lateral flow configuration. Furthermore, the comparison of multiplex format demonstrated low cross-reactivity and equal sensitivity to that of the singleplex assay. The optimized VFI platform thus provides a portable and rapid on-site monitoring system for multiplex biothreat detection with the potential for high sensitivity, specificity, reproducibility, and multiplexing capability, supporting its utility in remote and resource-limited settings.

A PON Monitoring System Integrating Fault Detection and Localization

We propose and experimentally demonstrate an integrated passive optical network monitoring system for fault detection and localization. Fault detection technology is based on the recognition of reflection spectrum generated by each single fiber Bragg grating, which can support large-capacity user fiber fault monitoring. Fault localization method uses two different total received powers measured by an optical power meter to solve the established equation, and it shows a good accuracy compared with the OTDR measurement results. Fault detection and localization are carried out separately but share the same transmitter module. The introduction of the additional module ensures that the troubleshooting does not affect other normal user data communication. We also investigate the dynamic range requirements of the fault detection technology and the measurement error of the fault localization method. The calculation results show the required dynamic range for the recognition device can be maintained within a small range and the use of high-accuracy optical power meter can reduce the measurement error. The proposed monitoring system can simultaneously provide a large-capacity fault detection technology and high-accuracy fault localization method in a cost-sensitive passive optical network market.

Face mask detection and social distance monitoring system for COVID-19 pandemic.

Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.

Bioinspired shark skin-based liquid metal triboelectric nanogenerator for self-powered gait analysis and long-term rehabilitation monitoring

Real-time gait monitoring is crucial for neuromuscular diagnosis and rehabilitation of neuromuscular ailments. However, the existing wearable sensors for gait analysis suffer from several drawbacks, such as external power requirement, poor sensitivity, short-term stability, and professional operator requirement, which restrict their applicability outside clinical settings. Here, a next-generation self-powered solid-liquid triboelectric nanogenerator-based flexible wearable sensor is developed. The proposed sensor is composed of a highly resilient liquid metal encapsulated within an innovative bio-mimicked shark skin-like microstructure embedded on the Ecoflex surface. The unique surface morphology imparts hydrophobicity to the solid triboelectric layer, which prevents the liquid metal adhesion during sensing and facilitates highly sensitive real-time monitoring of signals and long-term stability. The as-designed low-cost, highly scalable self-powered sensor, which is also compatible with diverse detection strategies, provides an on-demand user-friendly point-of-care gait detection and rehabilitation monitoring system with significant applications in personalized health care and sports science.

Leak detection in water distribution systems by classifying vibration signals

In addition to the issues of water quality and economic losses, leakages in pipe networks are a potential danger to public safety. Early detection of water leaks in pipe networks is crucial for implementing countermeasures to reduce structural damage and water losses. The method of data collection based on sensors installed in pipeline networks has gained a lot of attention due to its potential for application in real-time monitoring systems for leak detection. However, many models developed on data collected from simulations, engineered tests or field experiments, and few have been validated using real network data. To make further investigations in real water distribution systems (WDSs), machine learning (ML)-based leak detection models were developed and validated using signal data collected by piezoelectric accelerometers installed in WDSs over several cities of China. Nonlinear features describing the non-stationary characteristics contained in leak signals are extracted from the signals to build feature sets as inputs to the common classifiers, i.e., support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN). In addition, the spectrograms obtained by the short-time Fourier transform processing are used for the convolutional neural network SqueezeNet. The effects of different features, different dataset compositions, and different models for leak detection are compared and analyzed. SqueezeNet has the best performance with 95.15% accuracy in identifying leaks, and KNN is the best of the three classifiers with superior sensitivity and 88.17% accuracy in identification. This paper demonstrates the effectiveness and practicability of using machine learning models to identify leaks in real pipe networks. The application of machine learning models can perform effective leak detection with minimal human intervention, thus providing a meaningful contribution to the study of leak detection in real WDSs. The proposed model aims to identify leakages under operational conditions in real-life pipe networks with higher efficiency and reduced manual involvement. This paper demonstrates the applicability of accelerometers based on microelectromechanical systems for leak detection and establishes real network-based machine learning models; thereby, providing meaningful contribution to the literature regarding leak detection in a real WDS.

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).

A new comprehensive monitoring and diagnostic approach for early detection of mechanical degradation in helicopter transmission systems

Helicopters vulnerabilities specifically lie in single-load-path critical parts that transmit the engine’s power to the rotors. A fault in even one single trans- mission’s gear component may compromise the whole helicopter, yielding high maintenance costs and safety hazards. In this work, we present an effective di- agnosis and monitoring system for the early detection of the mechanical degra- dation in such components, also capable of providing insights on the damage’s causes. The classification task is performed by an ensemble of two learners: a convolutional autoencoder and a distance&density-based unsupervised classifier that use as regressors specific Health Indexes (HIs) and flight parameters. The proposed approach employs the autoencoder reconstruction error information to infer the most probable cause of each detected fault, and enacts post-processing filtering policies that effectively reduce the number of false alarms. Extensive experimental validation witnesses the good performances and the robustness of the proposed approach.

Design Of Non-Contact Human Body Temperature Detection Based On Internet Of Things (Iot) To Open The Door Automatically

Covid-19 has hit Indonesia since March 2020; as a policymaker, the government has imposed new regulations so that everyone can leave the house and do work without worrying about being exposed to the Covid-19 virus. Measurement of body temperature is the fundamental way to determine the condition of body temperature. However, the problem is that when a thermometer is used to check the temperature of the human body, it is still carried out in direct physical contact between humans so that the risk of transmission of the covid-19 virus can occur. Based on these problems, a thermometer was made, which can be used to check human body temperature without making physical contact. Using the prototype method and system design with UML diagrams, the temperature sensor GY-906 MLX90614 (Temperature detection sensor) was used to build a prototype non-contact human body temperature detection monitoring system based on the Internet of Things (IoT) to open doors automatically.). body), LCD (Liquid Crystal Display) displays the results of the human body temperature, servo motor as an action system in the form of a sliding door controller, ESP8266 Wifi Module, which is used as a microcontroller that processes sensor data and sends sensors through a wireless network. The data sent by the ESP8266 Wifi Module can be monitored via a Web application.