Real-time Information Acquisition Research Articles

Real-time Information Acquisition and Processing Method for Penetration Information Based on Multi-information Fusion

Real-time Information Acquisition and Processing Method for Penetration Information Based on Multi-information Fusion

Flexible Wearable Chemoresistive Ethylene Gas-Monitoring Device Utilizing Pd/Ti3C2Tx Nanocomposites for In Situ Nondestructive Monitoring of Kiwifruit Ripeness.

Kiwifruit, renowned for its antioxidant properties and nutritional richness, faces challenges in maintaining quality during transportation, often leading to suboptimal products reaching the market. To address this issue, a wireless transmission flexible ethylene monitoring device (WFEMD) was developed. This device comprises a flexible ethylene gas sensor and a signal transmission processing unit integrated with electronic components, enabling real-time monitoring capabilities. In this study, the catalytic activity of Pd and Pd/Ti heterojunctions was leveraged to enhance the ethylene gas sensing. The impact of Ti3C2Tx modified with varying masses of Pd nanoparticles on ethylene gas response levels was investigated. The signal transmission processing unit, fabricated by using the laser direct-writing method, was optimized to collect signals from the flexible ethylene gas sensor, convert them into corresponding ethylene concentrations, and transmit data via an antenna. By introducing a random forest (RF) classification algorithm, a remarkable 97.5% accuracy in predicting kiwifruit ripeness grades was achieved. The algorithm facilitated precise classification by collecting key parameters such as ethylene and CO2 during transportation. The WFEMD enables real-time acquisition of kiwifruit ethylene gas information, which is transmitted wirelessly for data visualization and traceability via mobile terminals. This empowers managers with timely insights into ethylene emissions and ripeness predictions, facilitating informed decision-making processes.

A tightly coupled GNSS RTK/IMU integration with GA-BP neural network for challenging urban navigation

Abstract Intelligent transportation system is increasing the importance of real-time acquisition of positioning, navigation, and timing information from high-accuracy global navigation satellite systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch real-time kinematic (RTK) GNSS/inertial measurement unit (IMU) integration algorithm with the assistance of genetic algorithm back propagation based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the root mean square error in the 2D and 3D component when the training and testing data are collected under 150 s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for GNSS signals discontinuously blocked with 500 s.

Fine grained analysis method for unmanned aerial vehicle measurement based on laser-based light scattering particle sensing

As an effective particle measurement method, laser-based particle sensors combined with unmanned aerial vehicles (UAVs) can be used for measuring air quality in near ground space. The Sniffer4D Mini2 features portability and real-time acquisition of accurate spatial distribution information on air pollution. Additionally, a new fine-grained analysis method called Co-KNN-DNN has been proposed to assess air quality between flight trajectories, allowing for a more detailed presentation of the continuous distribution of air quality. Therefore, this article introduces an unmanned aerial vehicle measurement fine-grained analysis method based on laser light scattering particle sensors. Firstly, the overall scheme was designed, M30T UAV was selected to carry the portable air quality monitoring equipment, with laser-based laser particulate matter sensor and Mini2, to collect AQI and related attributes of the near-ground layer in the selected research area, to do the necessary processing of the collected data, to build a data set suitable for model input, etc., to train and optimize the model, and to carry out practical application of the model. This article is based on the Co-KNN-DNN model for fine-grained analysis of air quality in spatial dimensions. Three experiments were conducted at different altitudes in the study area to investigate the practical application of fine-grained analysis of near-surface air quality. The experimental results show that the average R-squared value can reach 0.99. Choose to conduct experiments using the M30T UAV equipped with Sniffer4D Mini2 and a laser-based particulate matter sensor. The application research validates the effectiveness and practicality of the Co-KNN-DNN model.

Research on Combined Localization Algorithm Based on Active Screening-Kalman Filtering.

Real-time acquisition of location information for agricultural robotic systems is a prerequisite for achieving high-precision intelligent navigation. This paper proposes a data filtering and combined positioning method, and establishes an active screening model. The dynamic and static positioning drift points of the carrier are eliminated or replaced, reducing the complexity of the original Global Navigation Satellite System (GNSS) output data in the positioning system. Compared with the traditional Kalman filter combined positioning method, the proposed active filtering-Kalman filter algorithm can reduce the maximum distance deviation of the carrier along a straight line from 0.145 m to 0.055 m and along a curve from 0.184 m to 0.0640 m. This study focuses on agricultural robot positioning technology, which has an important influence on the development of smart agriculture.

Dynamic wake field reconstruction of wind turbine through Physics-Informed Neural Network and Sparse LiDAR data

Real-time acquisition of dynamic wake field information has garnered substantial attention in the wind farm industry, as it provides a crucial data source for intelligent wind farm monitoring and control. However, the existing wind measurement technologies, such as light detection and ranging (LiDAR), are limited to sparse data point measurements. This paper explores the utilization of a Physics-Informed Neural Network (PINN) to reconstruct wind turbine wake dynamics, specifically focusing on the influence of active wind turbine yaw operation on wake evolution. The methodology involves creating a tailored loss function that combines sparse wake measurement data with the Navier-Stokes (NS) equations. More precisely, the neural network incorporates the NS equations as constraints to guide the prediction of physical quantities in the output, including downwind velocity, crosswind velocity, and pressure. Taking the dynamic wake during yawing as a case study, the proposed method showcases remarkable universality and robustness across diverse scenarios involving varying scanning angle intervals, measurement point spacings, frequencies, and noise levels. It successfully captures the dynamic trends in wake evolution during yawing and accurately forecasts the wake trajectory and deflection. Even when tested with actual wake measurement data, the method can still effectively reconstruct the flow field, indicating significant potential for the real wind farm yaw control.

Intraoperative Laparoscopic Hyperspectral Imaging during Esophagectomy-A Pilot Study Evaluating Esophagogastric Perfusion at the Anastomotic Sites.

Hyperspectral imaging (HSI) is a non-invasive and contactless technique that enables the real-time acquisition of comprehensive information on tissue within the surgical field. In this pilot study, we investigated whether a new HSI system for minimally-invasive surgery, TIVITA® Mini (HSI-MIS), provides reliable insights into tissue perfusion of the proximal and distal esophagogastric anastomotic sites during 21 laparoscopic/thoracoscopic or robotic Ivor Lewis esophagectomies of patients with cancer to minimize the risk of dreaded anastomotic insufficiency. In this pioneering investigation, physiological tissue parameters were derived from HSI measurements of the proximal site of the anastomosis (esophageal stump) and the distal site of the anastomosis (tip of the gastric conduit) during the thoracic phase of the procedure. Tissue oxygenation (StO2), Near Infrared Perfusion Index (NIR-PI), and Tissue Water Index (TWI) showed similar median values at both anastomotic sites. Significant differences were observed only for NIR-PI (median: 76.5 vs. 63.9; p = 0.012) at the distal site (gastric conduit) compared to our previous study using an HSI system for open surgery. For all 21 patients, reliable and informative measurements were attainable, confirming the feasibility of HSI-MIS to assess anastomotic viability. Further studies on the added benefit of this new technique aiming to reduce anastomotic insufficiency are warranted.

A new intelligent cross-domain routing method in SDN based on a proposed multiagent reinforcement learning algorithm

PurposeA cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.Design/methodology/approachFirst, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.FindingsExperimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.Originality/valueMessage transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

A High Temporal-Spatial Resolution Power System State Estimation Method for Online DSA

The rapidly increasing integration of renewable energy sources aggravates the uncertainty and fluctuation in modern power system, which promotes the development of online dynamic security assessment (DSA). Real-time acquisition of high resolution temporal-spatial information on the system states lays the foundation for online DSA, while limited PMU installation and complex dynamic characteristics in real systems impose severe challenges to estimate system states with high quality in real time. This paper proposes a high temporal-spatial resolution state estimation (SE) method, leveraging graph convolutional network (GCN) and dense connectivity structure to estimate states of whole system at PMU reporting rate. Based on proposed SE method, an online DSA framework is developed for transient stability assessment (TSA), which only relies on the hybrid measurements accessible to the control centers in practice. Numerical experiment results in different scenarios demonstrate that the proposed SE method exhibits high SE accuracy and efficiency under different PMU observability. The performance improvement of SE-based TSA approach versus raw-measurements-based TSA approaches is also verified both theoretically and experimentally.

Parametric Characterization of a Tractor Engine by Specific Fuel Consumption

The paper highlights that equipping agricultural mobile machines with sensors and electronic controls enables the remote acquisition of real-time information regarding the technical condition of engine systems while operation. (Research purpose) The objective of this research is to formulate a methodology for determining the multi-parameter characteristics of specific effective fuel consumption. This is illustrated through an examination of the Deutz BF 6M 2012 C engine, installed in the of the Terrion ATM 4200 tractor. (Materials and methods) The electronic control system was examined through data analysis. Utilizing Logic Analyzer 8, a connected logic analyzer, facilitated the extraction of a multi-parameter characteristic related to the specific fuel consumption of the engine. This data was obtained from the CAN bus while the machine was in operation. A statistical data processing method was developed using the Statistica 10 program. Regression equations were formulated to illustrate the correlation between fuel consumption, crankshaft speed and engine torque. The statistical significance of the relationship between fuel consumption across the entire range of operating modes and the selected parameters was corroborated by the values of the coefficient of determination and Fisher’s criterion. (Results and discussion) The data from the multi-parameter characteristic, illustrating the correlation between fuel consumption, engine speed, and torque, aligns with the information provided by the manufacturing plant. This alignment further validates the accuracy of the derived regression equations. (Conclusions) The suggested sequence of steps for obtain a multi-parameter characteristic can be applied to other engine performance indicators. Monitoring operational performance to analyze information on the technical condition of machine components and assemblies is necessary for diagnostics and ensuring timely maintenance and repair.

Improving social resilience amid the COVID-19 epidemic: A system dynamics model.

Social resilience is a key factor in disaster management, but compared to resilience in other fields, research on social resilience is still limited to assessment or evaluation, and there is still a lack of dynamic and procedural research, which is also a challenge. This article constructs a causal feedback model and a system dynamics model of social resilience during the COVID-19 epidemic, so as to analyze the dynamic characteristics and improvement path of social resilience. After verifying the effectiveness of the model, model simulation is conducted and the following important conclusions are drawn: social resilience dynamically changes during the research cycle and is influenced by social entity behavior and social mechanisms; The sensitivity factors for the two variables that measure social resilience, namely panic degree and damage degree, are the real-time information acquisition of public and the epidemic awareness of local government, respectively. Therefore, the path to enhancing social resilience should be pursued from both the public and government perspectives, including improving the public's ability to access real-time information, increasing the timeline of government information disclosure, and enhancing local governments' understanding and awareness of the epidemic.

Buckwheat Plant Height Estimation Based on Stereo Vision and a Regression Convolutional Neural Network under Field Conditions

Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms.

A hybrid convolutional neural network with long short-term memory (HCNN-LSTM) model based Edge System Recommendation(ESR) for cloud service providers

Massive entities with significant state denaturation exist in the physical world, and users urgently want real-time and intelligent entity information acquisition. Now a days hand held devices and Electronic Device usage are increased rapidly. For effective cloud service edge systems are used. As rapid growth of internet and hardware technologies many edge providers are in the market. For Cloud Service Providers (CSP) it will be a chaos to choose optimised and reliable edge system for their usage. A hybrid convolutional neural network with Long Short-Term Memory (HCNN-LSTM) based Edge System Recommendation (ESR) Algorithm for Cloud Service Providers. An entity identification technique suitable for the edge is suggested to enhance the precision and responsiveness of entity state data search. It conducts accurate entity identification using the HCNN-LSTM while considering entity feature information. Even for new CSP optimised Edge System will be easily allocated. For this Recommendation Algorithm a total of 3 Servers and 25 Edge Systems with varied configuration are tested. 1000 GB hybrid data is used for transaction. The dataset collected from Kaggle online web-based repository. This proposed algorithm proves 95% more effective than existing techniques.

A few thoughts on the integrated space-air-ground emergency rescue communication command in China

Abstract. With the rapid development of remote sensing, geographic information (GI), artificial intelligence (AI) and other technologies, China is continuously strengthening emergency rescue innovation and numerous breakthroughs related to emergency rescue technologies and equipment which have been made through the National Key Research and Development Program. This paper first proposed and illustrated the concept, connotation and characteristics of integrated space-air-ground emergency rescue communication command from the perspective of mapping and geographic information, and then analysed the challenges faced by the integrated space-air-ground emergency rescue communication command in China. Subsequently, a space-air-ground emergency rescue command theoretical system is introduced by combining broadband emergency communication, accurate acquisition of real-time disaster information, disaster situation and scenario projection, emergency communication command services, and resilient emergency rescue command. The proposed integrated space-air-ground emergency communication and command design concept will be applied for China's disaster emergency rescue and command in the future to comprehensively improve the emergency rescue communication and command capability in China.

In vivo sensing to monitor tomato plants in field conditions and optimize crop water management

Irrigation is key to increasing crop yield and meeting the global demand for food. This study reports the assessment of tomato water consumption by bioristor, a new in vivo an Organic ElectroChemical Transistor-based biosensor. Bioristor enables direct, real-time acquisition of biophysical information about the plant’s water requirements directly from the plant sap, and thus the water input can be adjusted accordingly.The aim of this study is to demonstrate the efficacy of bioristor in rapidly detecting changes in the plant’s water status enhancing water use and irrigation efficiency in tomato cultivation with significant savings in the water supply. To this end, experiments were carried out in 2018 and 2020 in Parma (Italy) in tomato fields under different water regimes. The sensor response index (R) produced by bioristor recorded the real time plant health status, highlighting an excess in the water supplied as well as the occurrence of drought stress during the growing season. In both years, bioristor showed that the amount of water supplied could have been reduced by 36% or more. Bioristor also measured the timing and duration of leaf wetting: 438 h and 409 h in 2018 and 2020, respectively. These results open up new perspectives in irrigation efficiency and in more sustainable approaches to pesticide application procedures.

Optimized Design of Touching Parts of Soil Disinfection Machine Based on Strain Sensing and Discrete Element Simulation.

With the increasing level in the intensification of agricultural production in China, continuous cropping obstacles have become a problem that needs to be solved. The use of vertical rotary tillage technology and soil disinfection technology is an effective solution. In this paper, a vertical rotary soil-tilling variable disinfection combine was developed and an on-board control system with STM32 as the control core was designed to realize the real-time acquisition of powder monopoly torque information and the variable application of soil disinfection chemicals. Based on the obtained experimental soil parameters, a discrete element soil particle model was established, and orthogonal experiments were conducted to analyze the single-blade roller tillage process, and the optimal operating parameters were finally selected as 500 mm powder monopoly depth, 320 r/min knife roller speed, and 0.26 m/s forward speed, respectively. The field experiment found that the average tillage depth of the implement was 489 mm, the stability coefficient of tillage depth was 94.50%, the uniformity coefficient of soil disinfection was 85.57%, and the applied amount and the speed ratio coefficient of the given flow were linearly related, respectively. This research provides a technical reference for the deep tillage and soil disinfection of the powder monopoly.

Demosaicking DoFP images using edge compensation method based on correlation.

With the development of nanotechnology, the division of focal plane (DoFP) infrared polarization imaging system with real-time imaging has matured. Meanwhile, the demand for real-time acquisition of polarization information is growing, but the super-pixel structure of the DoFP polarimeter will bring instantaneous field of view (IFoV) errors. Existing polarization demosaicking methods cannot satisfy both accuracy and speed in terms of efficiency and performance. According to the characteristics of DoFP, this paper proposes an edge compensation demosaicking method by analyzing the channel correlations of polarized images. The method performs demosaicing in the differential domain, and the proposed method's performance is verified by comparison experiments using synthetic and authentic polarized images in the near-infrared (NIR) band. The proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency. It achieves an average peak signal-to-noise ratio (PSNR) improvement of 2 db on public datasets compared to current state-of-the-art methods. A typical 768 × 1024 specification short-wave infrared (SWIR) polarized image can be processed in 0.293s on the Intel Core i7-10870 H CPU, and the technique significantly outperforms various existing demosaicking methods.

A non-stationary channel prediction method for UAV communication network with error compensation

In an unmanned aerial vehicle (UAV) communication network, especially for mission-critical applications, ultra-reliable and low-latency communication (URLLC) of the control links has essential implications for realizing collision avoidance and real-time control of UAVs. However, the UAVs’ mobility and the channel environment’s variability may make the wireless channels highly non-stationary. Real-time and accurate Channel Status Information (CSI) acquisition is a critical challenge to implementing URLLC for UAV control links. Predicting the characteristics of non-stationary channels is profitable for formulating communication strategies to mitigate the effects of future channel fading. Nevertheless, the difficulty lies in extracting accurate prediction models for non-stationary channels to obtain accurate CSI. This paper proposes a more precise prediction by utilizing the CSI obtained as feedback from the receiver and introducing error correction. The method contains three stages, CSI collection and processing, channel tracking, and error correction. First, the transmitter collects the CSI feedback from the receiver and converts it to a stationary series. Secondly, the recent historical CSIs are used to track the time evolution of the channel based on an autoregressive integrated moving average (ARIMA) model. Next, the Gaussian process regression (GPR) model is used to establish the prediction error regression model and obtain a more accurate prediction. Finally, the effectiveness of the proposed method is verified based on a UAV wireless communication network application scenario in an urban environment. Simulation results show that the proposed method outperforms other methods regarding RMSE and reliability in high-dynamic UAV communication scenarios. Significantly when the channel changes rapidly, this method can respond to the changes faster and predict the CSI more accurately.

Miniature Noninvasive Sensor Based on Impedance-Change Detection in Branches for Measuring Branch Ice Content in Overwintering Woody Plants.

Advancements in detection instruments have enabled the real-time acquisition of water information during plant growth; however, the real-time monitoring of freeze-thaw information during plant overwintering remains a challenge. Based on the relationship between the change in the water-ice ratio and branch impedance during freezing, a miniature noninvasive branch volume ice content (BVIC) sensor was developed for monitoring real-time changes in volumetric ice content and the ice freeze-thaw rate of woody plant branches during the overwintering period. The results of the performance analysis of the impedance measurement circuit show that the circuit has a lateral sensitivity range, measurement range, resolution, measurement accuracy, and power consumption of 0-35 mm, 0-100%, 0.05%, ±1.76%, and 0.25 W, respectively. The dynamic response time was 0.296 s. The maximum allowable error by the output voltage fluctuation, owing to the ambient temperature and humidity, was only ±0.635%, which meets the actual use requirements. The calibration curve fit coefficients were >0.98, indicating a significant correlation. The ice content of plant branches under cold stress was measured for indoor and field environments, and the sensors could effectively monitor changes in the branch ice content in plants exposed to cold stress. Additionally, they can differentiate between plants with different cold resistances, indicating the reliability of the BVIC sensor.

Application research of Welding process monitoring system based on LAN

In the field of production and manufacturing, the monitoring of welding information and analysis of post-welding signal is an important technical means to ensure the overall quality of work. Monitoring the information parameters in the welding process and comprehensively mastering the construction process of welding process can not only quickly obtain the working status of the staff, but also take the collected signal as the basis for quality evaluation. Based on the research status of welding process monitoring system in the new era, this paper discusses the design of monitoring system for real-time acquisition of welding process information, and makes clear the application principle of signal analysis, management module and quality evaluation system. The final results show that the overall system design can not only manage the real-time data, but also optimize the welding technology and improve the quality of welding work.