Real-time Information Collection Research Articles

Revolutionizing Agriculture IoT and Random Forest Algorithm for Crop Monitoring and Disease Prediction

This research about presents a groundbreaking approach to revolutionize farming through the integration of Web of Things (IoT) innovation and progressed machine learning calculations. Centering on the improvement and execution of an IoT-based edit checking framework coupled with the Random Forest calculation for malady expectation, the ponder points to improve agrarian hones and relieve trim misfortunes caused by infections and natural components. Real-time information collection from IoT sensors sent in rural areas empowers comprehensive checking of vital natural parameters such as temperature, mugginess, soil dampness, and light concentrated. The Random Forest calculation analyzes this information to precisely foresee trim maladies, giving ranchers with significant bits of knowledge for proactive illness administration. Test comes about illustrate the adequacy of the proposed approach, with the Random Forest show accomplishing an exactness of 92%, exactness of 93%, review of 91%, and F1-score of 92%. These comes almost defeat customary methodologies and existing explore works, highlighting the potential of IoT and machine learning for optimizing alter proficiency and ensuring around the world food security.

Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data

Smart cities leverage advanced technologies to enhance urban living through the real-time collection, processing, and analysis of contextual information. The potential to improve residents’ outdoor experiences in these cities increases dramatically as smart technologies are integrated into urban environments, making them more interconnected. It helps to explore the pivotal role of real-time data in optimizing various aspects of city management, focusing on key domains such as traffic, public transportation, emergency response, waste management, and environmental monitoring. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. This model addresses the dynamic and context-aware nature of Smart Cities by proposing a novel framework for real-time contextual information prediction and personalized outdoor activity suggestions for users. Leveraging the vast amount of data generated by Smart City infrastructure, this study integrates advanced data analysis techniques with deep learning models to enhance the urban living experience. A variety of datasets, such as those on the weather, air quality, traffic patterns, event schedules, and user activity patterns, are gathered and analyzed as part of the methodology. This data are processed and interpreted using machine learning algorithms, which find correlations, trends, and patterns that affect outdoor activities. Suggestions for appropriate outdoor activities can be generated in real time based on contextual information, past behavior, and user preferences. The framework begins by collecting and processing diverse datasets from sensors, Internet of Things (IoT) devices, and other urban sources to create a comprehensive understanding of the current city context. Deep learning models, such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), are employed to analyze this data and predict real-time contextual information, including weather conditions, traffic patterns, and social events. It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. The result shows that the proposed air pollution model predicted 96.06700 PM2.5 concentration levels, subsequently the temperature model predicted 14.06800∘C. The integration of real-time contextual information prediction and personalized outdoor activity suggestions showcases the potential for creating more engaging and user-centric Smart City ecosystems. This research attempts to provide personalized recommendations that are in line with users’ preferences, the state of the environment at the time, and other pertinent contextual factors by utilizing data from multiple sources, including IoT devices, mobile applications, and environmental sensors.

Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application

This Eye gaze following is the real-time collection of information about a person's eye movements and the direction of their look. Eye gaze trackers are devices that measure the locations of the pupils to detect and track changes in the direction of the user's gaze. There are numerous applications for analyzing eye movements, from psychological studies to human-computer interaction-based systems and interactive robotics controls. Real-time eye gaze monitoring requires an accurate and reliable iris center localization technique. Deep learning technology is used to construct a pupil tracking approach for wearable eye trackers in this study. This pupil tracking method uses deep-learning You Only Look Once (YOLO) model to accurately estimate and anticipate the pupil's central location under conditions of bright, natural light (visible to the naked eye). Testing pupil tracking performance with the upgraded YOLOv7 results in an accuracy rate of 98.50% and a precision rate close to 96.34% using PyTorch.

Nearly monodisperse ZnS nanoparticles for portable recognition, enrichment and detection of Cd(II)

As cadmium ions (Cd2+) pose a serious threat to environment and human health, it is significant to develop portable, fast-response and sensitive Cd2+ sensors for both real-time and on-site collection of such critical information. In this study, nearly monodisperse ZnS nanoparticles (NPs) were prepared and used to recognize, enrich and detect Cd2+ ions. ZnS NPs demonstrated fast adsorption kinetics towards Cd2+ ions (removing 99.3 % of Cd2+ in 12 min), surpassing previously reported multiple Cd2+ adsorption materials. Upon adsorbing Cu2+ and Cd2+ ions via substitution reaction, ZnS NPs gain strong peroxidase activity, and demonstrate the ability to selectively detect Cu2+ and Cd2+ ions among common metal ions. By further conjugating with our previous HSnS NFs (Hollow SnS nanoflower) nanosensor to eliminate the interference of Cu2+, the ZnS NPs nanosensor could selectively detect Cd2+ ions with recoveries above 80 %. The ZnS NPs nanosensor can cover a wide range of Cd2+ concentration from 0.5 to 100 μg L−1 with an extremely low detection limit of 0.38 μg L−1. When coupling to a portable RGB sensor, as low as 0.5 μg L−1 Cd2+ ions could be reached on-site for river water, giving an outstanding recovery of 99.4 %. The current results provide a new avenue to the on-site detection of Cd2+ ions, and would doubtlessly contribute to the future construction of portable and intelligent sensor arrays towards complex environmental samples.

Multi-Terminal Realization on Construction Engineering Safety and Quality Supervision Integrated Platform Based on Real-Time Image Information Collection Network

Multi-Terminal Realization on Construction Engineering Safety and Quality Supervision Integrated Platform Based on Real-Time Image Information Collection Network

Intelligent machine plus production line digital twin model construction technology

Digital twin is one of the key technology of intelligent digital manufacturing field, the related research is still in the initial stage This article in complex parts machining production line as the research object, from the geometric physical behavior rules four aspects to explore the digital twin modeling technology of production line Through the geometric model and the size of the physical model visualization expression line shape Based on the three-layer model of geometric physical behavior, the digital twin behavior model is constructed to describe the operation failure rule of the machine and unit. Driven by real-time information collection and fusion technology, closed-loop control and iterative optimization are realized Finally, the application value of digital twin technology in intelligent monitoring information feedback decision control is demonstrated through the intelligent control system of machining production line.

Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs

In the process of high-speed penetration, penetrating ammunition is prone to problems such as penetration overload signal vibration and mixings and projectile attitude deflection. It is easy to misjudge if a fuze relies only on the overload data from the ground or the utilized program, and the actual penetration overload measured under actual launch conditions cannot be taken as the dynamic judgement basis. Therefore, a real-time penetration overload prediction method based on a deep neural network is proposed, which can predict overload values according to the projectile parameter settings, the real-time collection of overload information, and the calculation speed and assist the fuze in judging the target layer and projectile attitude. In this paper, we adopt a deep learning model with multiple time series inputs and modify the input coding mode so that the model can output a 48 μs overload curve within 20 μs, meeting the real-time signal processing requirements of the high-speed missile penetration process. The mean squared error between the predicted curve and the actual curve is 0.221 for the prediction of multilayer penetrating targets and 0.452 for the prediction of thick penetrating targets. A penetration overload prediction function can be realized.

Security Risk Management System for the Construction and Operation of Smart Port Area Based on BP Neural Network Algorithm

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the BP neural network algorithm. By establishing and analyzing a safety risk management system model based on the BP multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority!

Comparative Experiment on TTP Classification with Class Imbalance Using Oversampling from CTI Dataset

Cyber threat intelligence (CTI) refers to the real-time collection of threat information and analysis of these acquired data to identify the situation and attack mechanism of a security threat. In a CTI analysis, it is important to have a standardized attack model. Recently, the MITRE adversarial tactics, techniques, and common knowledge (ATT&CK) framework has been widely used as the de facto standard security threat modeling technique. However, analyzing a large amount of data using the tactics, techniques, and procedures (TTP) of ATT&CK with a limited number of security personnel is time-consuming. To solve this cost-sensitive issue, research on automated classification of TTP from CTI data using artificial intelligence techniques is currently underway but remains challenging. This is because CTI data are domain-specific, and therefore, it is difficult to obtain labeling data to be used as training data for AI models. Hence, the distribution of training data related to TTP labeling is imbalanced. Thus, the current accuracy of ML-based TTP classification is still around 6080%. This study aims to improve the TTP classification accuracy from unstructured CTI data using machine learning while mainly focusing on solving the problems of small training datasets and TTP class imbalance. Therefore, we proposed a TTP classification method by applying easy data argumentation (EDA) and compared its performance with those of previous studies. By applying the proposed methodology, a 60–80% improvement was observed compared to the reference baseline model, TRAM. This indicates that the preprocessing methodology of applying the EDA technique is effective at improving the performance of TTP classification from unstructured CTI data in the CTI domain.

Automated Construction Progress Monitoring of Prefabricated Concrete Structures using Building Information Modeling and Internet of Things

The construction industry in India is facing various issues pertaining to low productivity, inefficient management at construction sites, high construction safety risk and lack of information on the status of project. With the development of modern tools and technologies prefabricated construction is becoming more prevalent in the building industry. However, there are many limitations in the actual manufacturing process such as storage, quality checks, installation of the prefabricated components and its status which is due to the lack of real-time information collection and sharing. In order to speed up the construction process and better management and monitoring of the project, this research proposes the framework for integration of Internet of Things (IoT) and Building Information Modeling (BIM). The paper proposes a conceptual framework of an IoT enabled BIM platform to accomplish a real-time visualization in prefabricated concrete structures construction. This is done by using various sensors in the prefabricated components to demonstrate a highly efficient automated progress monitoring system of prefabricated construction.

Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.

Soil Moisture Content Prediction Model for Tea Plantations Based on a Wireless Sensor Network

<p>Suitable soil moisture content (SMC) can not only increase the ability of tea tree roots to absorb and utilize nutrients but also improve the utilization rate of soil nutrients, which can ensure a continuous and stable yield and tea leaf quality. Traditional methods for predicting soil water content generally have low accuracy and efficiency problems. A real-time soil information collection system based on a wireless sensor network was built, and a new predicting SMC Model (AO-SVM) for tea plantations using support vector machine optimized (SVM) by Aquila Optimizer (AO) was constructed and evaluated. The SMC prediction model was established using weather data, soil temperature (ST), soil electrical conductivity (SEC), and PH value (pH), and soil water potential (SWP), and so on. First, the correlation between individual SMC, ST, SEC, pH, SWP was analyzed and parameters with high correlation with soil water content were subsequently identified. The AO-SVM model was utilized to predict the soil moisture content. The experiments showed that the R2 of AO-SVM model proposed in this paper is 0.925. It indicates that the AO-SVM model is effective and feasible and achieves advantageous performance over long short term memory (LSTM), generalized regression neural network (GRNN), the opposition-based chaotic salp swarm algorithm optimized SVM (OCSSA-SVM), sparrow search algorithm optimized SVM (SSA-SVM), particle swarm optimization SVM (PSO-SVM), and the whale algorithm optimized SVM (WOA-SVM) model, which can help guide the irrigation and fertilization management of tea plantations.</p> <p> </p>

Automatic Classification and Coding of Prefabricated Components Using IFC and the Random Forest Algorithm

The management of prefabricated component staging and turnover lacks the effective integration of informatization and complexity, as relevant information is stored in the heterogeneous systems of various stakeholders. BIM and its underlying data schema, IFC, provide for information collaboration and sharing. In this paper, an automatic classification and coding system for prefabricated building, based on BIM technology and Random Forest, is developed so as to enable the unique representation of components. The proposed approach starts with classifying and coding information regarding the overall design of the components. With the classification criteria, the required attributes of the components are extracted, and the process of attribute extraction is illustrated in detail using wall components as an example. The Random Forest model is then employed for IFC building component classification training and testing, which includes the selection of the datasets, the construction of CART, and the voting of the component classification results. The experiment results illustrate that the approach can automate the uniform and unique coding of each component on a Python basis, while also reducing the workload of designers. Finally, based on the IFC physical file, an extended implementation process for component encoding information is designed to achieve information integrity for prefabricated component descriptions. Additionally, in the subsequent research, it can be further combined with Internet-of-Things technology to achieve the real-time collection of construction process information and the real-time control of building components.

Methodology for Monitoring the Mobility of Circular Labour Migrants in Moscow Region

The paper presents the original author’s methodology for monitoring commuting labour migration in a region on the example of Moscow Region. The methodology is based on regular collection of real-time information from the Yandex.Probki platform on the regional transport network state by querying and saving travel time for a fixed set of car routes (basic sample) covering the region under study. An analysis of the data collected over the past two years enabled studying the structure and dynamics of travel time losses by commuting labour migrants. The time dynamics of losses are sensitive to events such as lockdowns and holidays. The estimates obtained show a stable cyclicity of travel time losses within a day and a week, which confirms the validity of the indicator proposed by the author. The study demonstrates that the loss of time on the commute during peak hours is 2.5 times higher than the loss when driving without traffic jams. In conclusion, the paper discusses the prospects for scaling the author’s methodology to any regions in which the Yandex.Probki platform is present.

A Framework for Prefabricated Component Hoisting Management Systems Based on Digital Twin Technology

The hoisting of prefabricated components (PCs) is a key step during the construction of prefabricated buildings. Aiming at the problems existing in the control of PC hoisting, an innovative hoisting management system framework based on the digital twin (DT) is established in this paper. The system framework comprehensively utilizes the building information model (BIM) and Internet of Things (IoT) to establish a digital twin model (DTm) for PC hoisting control and uses Dijkstra’s algorithm to conduct hoisting route planning according to the BIM data in the model. Meanwhile, long-range radio (LoRa) technology was used for data acquisition and transmission to monitor the movement state of the PCs in the hoisting process. By testing it in a prefabricated building project, the DT control method was conducted to realize the functions of real-time information collection, hoisting path planning and PC positioning, which proved the feasibility and effectiveness of the method. As a key technology to realize intelligent manufacturing, DT has been widely studied in academia. The DTm of the hoisting process of PCs is established in this study; it improves the level of intelligent management of prefabricated building construction and provides a new idea for intelligent building construction.

Multisensory Testing Framework for Advanced Driver Assistant Systems Supported by High-Quality 3D Simulation.

ADAS and autonomous technologies in vehicles become more and more complex, which increases development time and expenses. This paper presents a new real-time ADAS multisensory validation system, which can speed up the development and implementation processes while lowering its cost. The proposed test system integrates a high-quality 3D CARLA simulator with a real-time-based automation platform. We present system experimental verifications on several types of sensors and testing system architectures. The first, open-loop experiment explains the real-time capabilities of the system based on the Mobileye 6 camera sensor detections. The second experiment runs a real-time closed-loop test of a lane-keeping algorithm (LKA) based on the Mobileye 6 line detection. The last experiment presents a simulation of Velodyne VLP-16 lidar, which runs a free space detection algorithm. Simulated lidar output is compared with the real lidar performance. We show that the platform generates reproducible results and allows closed-loop operation which, combined with a real-time collection of event information, promises good scalability toward complex ADAS or autonomous functionalities testing.

Outlier Detection Method for Flash Flood Disaster Monitoring Data based on Information Entropy

In the research of flash flood disaster monitoring and early warning, the Internet of Things is widely used in real-time information collection. There are abnormal situations such as noise, repetition and errors in a large amount of data collected by sensors, which will lead to false alarm, lower prediction accuracy and other problems. Aiming at the characteristic that outliers flow of sensors will cause obvious fluctuation of information entropy, this paper proposes a local outlier detection method based on information entropy and optimized by sliding window and LOF (Local Outlier Factor). This method can be used to improve the data quality, thus improving the accuracy of disaster prediction. The method is applied to data stream processing of water sensor, and the experimental results show that the method can accurately detect outliers. Compared with the existing detection methods that only use data distance to determine, the test positive rate is improved and the false positive rate is reduced.

Design of Smart Home Control System Based on Wireless Voice Sensor

With the rapid development of economy, science, and technology, people have higher and higher requirements for the quality of home life, and smart home systems are required to have higher convenience and comfort. The continuous development of embedded technology, short-range wireless communication technology, and microcontroller technology provides a new development direction for the research and development of smart home systems. Under the premise of fully studying the research status and development trend of smart home systems, this paper combines embedded technology, ZigBee wireless communication technology, voice recognition technology, GSM communication technology, etc., and proposes a ZigBee-based smart voice home appliance control system. The system adopts ZigBee wireless communication to form a comprehensive management system for household electrical appliances, electronic and electrical equipment, etc., for household electrical control, intelligent security, and environmental monitoring. This paper designs the overall scheme of a smart home control system based on voice recognition technology. According to this scheme, the hardware and software of the system are designed, and the wireless communication method combining Wi-Fi and RF (radio frequency) of the system is also determined. In order to make the system more humane, two sets of control schemes, voice control and button control, are designed according to the system’s functional requirements. In the voice control solution, the user first reads the control commands set in the voice library on the smart terminal, and then, the system automatically recognizes the commands read by the user. After the recognition is successful, the user can control the home appliances through the command. In the key control scheme, users can control home appliances through keys on the smart terminal. The upper computer transplants the audio library and the algorithm studied in this paper to realize the audio collection and storage, the confirmation of family members, and the recognition of voice commands; the lower computer is used for real-time collection of environmental information and wireless control of home facilities. After the system is started, the family members are first confirmed by voice, and then, the voice command recognition result and real-time environmental information jointly decide to form a control command. The upper computer sends the control command to the lower computer through the serial port to control the corresponding home equipment. The system is tested and analyzed under experimental conditions. The test results show that the system can control home facilities through voice commands combined with real-time environmental information while confirming family members.

Research on Data Gateway for Ship Based on Dynamic Adaptation

In view of the immature status of the old-fashioned ship information collection and data analysis system in inland rivers, after analyzing the characteristics of ship navigation information, the problem of low efficiency, inaccuracy and poor real-time information collection is solved. Starting from ship equipment (such as main engine, power station, control system, etc), a ship data acquisition gateway based on dynamic adaptation is designed. Based on the principle of making full use of the old system of marine equipment in the engine room, a multi-protocol data acquisition method based on an adaptive model is constructed, which realizes the separation of information acquisition and information use, eliminates all the close coupling relations between system equipment and applications, and realizes the integration of different data information on the ship and the dynamic display management function on the client.

Research on the Design of Intelligent Data Acquisition Platform for Wireless Monitoring of Rotating Machinery Based on Arm

Rotating machinery is the most extensive type of machinery in the industry, which is the key equipment in many industries. Therefore, once the rotating machinery fails, it will cause huge economic losses, which requires us to monitor and diagnose the equipment status. Therefore, the rotating mechanical equipment must have an intelligent data acquisition system, which can collect the state of mechanical operation. Through real-time information collection, the rotating machinery can be monitored and diagnosed in real time, which will reduce the accident shutdown rate and maintenance costs. Therefore, the rotating machinery can reduce the production cost, which will avoid rabbit accidents and ensure the safety of production. Arm is an embedded computer system, which can be used for intelligent data collection flexibly. Firstly, this paper analyzes the application of embedded system in the rotating machinery monitoring system. Then, the hardware scheme and software of the system are designed.