Accuracy Of Data Acquisition Research Articles

Design and Implementation of Data Analysis System of Social Network

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.

Data Collection Method for Energy Storage Device of Distributed Integrated Energy Station based on Double Decision Tree

The distributed integrated energy station includes an electric energy storage device, heat storage device, cold storage device and other devices. Aiming at the problem of low data acquisition accuracy of energy storage device caused by using a single sensor or acquisition scheme in the existing methods, a new data acquisition method of energy storage device of distributed integrated energy station is designed based on double decision tree algorithm. The data acquisition process of double decision tree algorithm is constructed. On the basis of the process, the mathematical models of electric energy storage device, heat storage device, cold storage device and hybrid energy storage device are established. Then the double decision tree algorithm is used to solve the constructed model, and the acquisition pseudo code is given. So far, the data acquisition of distributed integrated energy station energy storage device based on double decision tree has been completed. The results of case analysis show that the accuracy of this method is higher than 98%, and the collection time is less than 30 ms.

Network monitoring and processing accuracy of big data acquisition based on mathematical model of fractional differential equation

Abstract Aiming at the lack of subjectivity of the network security situation assessment method and the complexity and non-linearity of data obtained through situational factors, a fuzzy neural network security situation which is optimised based on an improved gravitational search algorithm combined with fractional differential equation analysis, as an Evaluation model, is proposed. In order to quickly and accurately predict the situation value of the network security situation at that moment, a method for situation prediction of long-term and short-term memory networks based on an improved Nadam algorithm to optimise the online update mechanism is proposed. Note that the situation time series obtained from online assessment cannot be used in a better and efficient manner. The model can minimise the cost function and update the model more effectively by updating the model parameters online Prediction accuracy. In order to improve the problem of slow convergence speed during model network training, the Look-ahead method is used to improve Nesterov's adaptive gradient momentum estimation algorithm to accelerate the model's convergence. Finally, the simulation results analyse and compare the prediction model, which not only improves the convergence speed of the prediction model, but also greatly reduces the prediction error of the model.

Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids

Wireless sensor networks are used to monitor the operating status of the microgrids, which can effectively improve the stability of power supplies. The topology control is a critical issue of wireless sensor networks, which affects monitoring data transmission reliability and lifetime of wireless sensor networks. Meanwhile, the data acquisition accuracy of wireless sensor networks has a great impact on the quality of monitoring. Therefore, this paper focuses on improving wireless sensor networks data acquisition satisfaction and energy efficiency. A joint acquisition time design and sensor association optimization algorithm is proposed to prolong the lifetime of wireless sensor networks and enhance the stability of monitoring, which considers the cluster heads selection, data collection satisfaction and sensor association. First, a multi-constrained mixed-integer programming problem, which combines acquisition time design and sensor association, is formulated to maximize data acquisition satisfaction and minimize energy consumption. To solve this problem, we propose an iterative algorithm based on block coordinate descent technology. In each iteration, the acquisition time is obtained by Lagrangian duality. After that, the sensor association is modeled as a 0–1 knapsack problem, and the three different methods are proposed to solve it. Finally, the simulations are provided to demonstrate the efficiency of the algorithm proposed in this paper.

A Mathematical Formulation for Establishing the Relationship of Accuracy and Configuration of Data Acquisition in Close Range Photogrammetry. (Dept. C)

Configuration of data acquisition in close range photogrammetry is defined by (1) the base, which is the distance between the exposure stations of a stereopair, (2) the object distance, which is the perpendicular distance from the center (midfield) of object space to the base, and (3) the convergence of the two camera axes. The accuracy of object space coordinates of points is a function of configuration. A mathematical formulation establishing the relationship of accuracy and configuration is derived and then applied to obtain the optimum configuration, that gives the best accuracy in close range photogrammetry applications. The formulation developed is general and can be applied to calculate the expected accuracy of any given configuration, or to design a configuration that will yield a required accuracy.

Retraction.

Retraction: "Role of RKIP in human hepatic stellate cell proliferation, invasion, and metastasis", by Quanfang Huang, Jinbin Wei, Ling Wei, Xiaolin Zhang, Facheng Bai, Shujuan Wen, Yuanyuan Wei, Shimei Tan, Zhongpeng Lu, and Xing Lin, J Cell Biochem. 2019; 6168-6177: The above article, published online on 10 October 2018 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/10.1002/jcb.27904) has been retracted by agreement between the authors, the journal's Editor in Chief, Prof. Dr. Christian Behl, and Wiley Periodicals LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. The investigation was conducted by several experts into the accuracy of the data acquisition and concluded that inappropriate manipulation had taken place during Western blotting. Specifically, inconsistencies and duplicated image elements were found in figures 1A, 1B, 1D, 2C, 5D, and 6A. The authors disagree with this decision.

Automatic Fault Diagnosis Method for Hydrodynamic Control System

In order to improve the accuracy and speed of fault data acquisition of fluid power control system, this paper designs an automatic fault diagnosis method of fluid power control system based on English translation speech recognition. Firstly, the SDG model of the fluid dynamic control system is established, and the fault link is obtained and determined. Then the correlation dimension of data in fluid mechanics calculation is analyzed, and the fault data location is realized. On this basis, the fault classification model of the hydraulic power control system is established, and the automatic fault diagnosis of the hydraulic power control system is completed. Experiments show that the new fault diagnosis method can effectively improve the accuracy and speed of fault data acquisition of fluid power control system, the highest accuracy can reach 89.92%, the fastest speed is 11s, and improve the reliability of fault diagnosis results.

Internet-of-things-based four-dimensional high-density electrical instrument for geophysical prospecting

Abstract. The high-density electrical method is a primary method used in shallow geophysical prospecting. Due to the rapid industrial development that has taken place in recent years, the function and performance of high-density electrical instruments have been considerably improved in several aspects. However, most of the electrical instruments currently available on the market still exhibit some shortcomings, such as being bulky, heavy, limited in their data acquisition accuracy, and difficult to connect to the Internet for remote monitoring. To address these problems, this study developed a new multifunctional four-dimensional (4D) high-density electrical instrument based on remote wireless communication technology. The system is small and lightweight, includes an integrated transceiver, has high data acquisition accuracy, and is capable of remote wireless real-time control. In this study, the hardware circuit was designed. The Arm all-in-one (AIO) LJD-eWinV5-ST7 with a 154.4 cm × 87 cm, 800 × 480 high-brightness wide-temperature-range display is used as the host computer, which has the advantages of small size, low power consumption, and abundant hardware resources. Internet of things (IoT) technology is incorporated in the system, and a 4G module is employed to provide a real-time remote control and data acquisition monitoring system based on the cloud platform. Tests showed that this instrument is stable and convenient to use and can meet the requirements for use in field prospecting.

Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong

Urban trees exhibit a wide range of ecosystem services that have long been unveiled and increasingly reported. The ability to map tree species and analyze tree health conditions would become vividly essential. Remote sensing techniques, especially hyperspectral imaging, are being evolved for species identification and vegetation monitoring from spectral reponse patterns. In this study, a hyperspectral library for urban tree species in Hong Kong was established comprising 75 urban trees belonging to 19 species. 450 bi-monthly images were acquired by a terrestrial hyperspectral camera (SPECIM-IQ) from November 2018 to October 2019. A Deep Neural Network classification model was developed to identify tree species from the hyperspectral imagery with an overall accuracy ranging from 85% to 96% among different seasons. Representative spectral reflectance curves of healthy and unhealthy conditions for each species were extracted and analyzed. The hyperspectral phenology models were developed to achieve high accuracy and optimization of data acquisition. The bi-monthly canopy signatures and vegetation indices revealed different seasonality patterns of evergreen and deciduous species in Hong Kong. We explored the utility of terrestrial hyperspectral remote sensing and Deep Neural Network for urban tree species identification and characterizing. This provides a unique baseline to understand hyperspectral characteristics and seasonality of urban tree species in Hong Kong that can also contribute to hyperspectral imaging and database development elsewhere in the world.

A Mathematical Formulation for Establishing The Relationship of Accuracy and Configuration of Data Acquisition in Close Range Photogrammetry.(Dept.C)

A Mathematical Formulation for Establishing The Relationship of Accuracy and Configuration of Data Acquisition in Close Range Photogrammetry.(Dept.C)

Supervised learning vs. unsupervised learning: A comparison for optical inspection applications in quality control

For the establishment of a successful quality management system in companies, the quality control of e.g. newly produced goods or the return of old and used parts is an essential component. One solution for this is the optical inspection of the surface of objects with the help of image processing algorithms. Using the case study of printer cartridges, this paper evaluates the extent to which different methods of machine learning can contribute to a successful quality control. Established methods of supervised learning have the advantage that they are already proven in many applications and have a very high detection accuracy. However, they require a lot of labelled training data and this high effort also means high integration costs. A new approach is a data-reduced variant from unsupervised learning. Here, the algorithm is trained only with defect free objects, for example as they come to a large extent from the production. If the objects are defective, the method from the field of anomaly detection or even novelty detection detects something that is different from the learned norm. This has the advantage that not all defects have to be known beforehand. And this in turn avoids acquiring a large amount of training data for each of these defects. This paper compares the effort required to acquire training data and compares it with the detection accuracy of the different methods in order to give an assessment of the extent to which the use of unsupervised learning methods is beneficial. Newly produced and used printer cartridges are used for this purpose. Image data is acquired from 18 different printer cartridge models. Afterwards they are fully annotated (labelled). A smart separation into training, validation and test data allows the training of supervised and unsupervised methods as well as a complete evaluation regarding the effort for data acquisition, annotation and detection accuracy of the defects. Finally, an outlook for chances and risks of the respective procedures is given.

Analysis of Data Acquisition Accuracy with UAV

Due to the increasingly advanced digital devices, high resolution cameras and utility of fast processing, Unmanned Aerial Vehicles (UAV) have become a prominent feature of the various remote sensing procedures. Because the benefits of the technology may justify the use of data in topographic mapping, cadastral mapping or even engineering geodesy, we have conducted studies to determine the accuracy that can be achieved using UAV as an integrated data acquisition tool. First, we developed a test field in rural area for investigations. The results of the test flights conducted there were determined that what conditions influence the accuracy of the survey.

Theoretical Studies and Implementation on the Temporary Data Storage Method for Cone Penetration Test

The traditional cone penetration test system uses cable to transmit data; as the probe goes deeper into the ground, the length of the cable will become longer. This makes the installation of the test equipment more complicated, and excessively long cables cause signal distortion and seriously affect data accuracy. To simplify the experimental equipment and improve the accuracy of data acquisition, a cableless cone penetration test system is proposed. The improved system uses an SD card to store the experimental data, as opposed to using cables for communication which, often lead to the distortion of signals caused by long-distance communication and data loss caused by accidental cable breaks. Therefore, the accuracy of the collected data is higher, and the experimental device is simplified. To evaluate the applicability and efficiency of our design, we have carried out exploration experiments with the sensor system proposed in this paper. The test results show that the experimental data collected by the new system are basically consistent with the data collected by traditional cable CPT equipment, and the accuracy of the collected data is higher. It is more reliable and accurate to analyze the comprehensive mechanical properties of the soil layers with the data collected by the new system.

Accuracy and reproducibility of 3D data acquisition for human teeth using intra-oral scanners: A forensic odontological approach

Three-dimensional (3D) scanning technologies are a new development in the area of forensic dentistry. Dental measurements also called as odontometrics can provide forensic odontologist an effective and reliable approach to evaluate the age, sex and biogeographical ancestry. The present study aims to compare and evaluate the accuracy of the acquired 3D dental model through manual and digital linear measurements. Sixty-three extracted human teeth were used in the study. The manual metrics obtained using digital vernier caliper served as reference values, and the digital models of teeth were created using an intraoral 3D scanner and further measurements were recorded through 3D propriety software. A parametric two sample T-test showed statistical agreement among the four parameters for premolars and five parameters for molars out of seven total parameters. The work suggests digital technology may serve as an alternative technique over physical measuring techniques, but certain structured standards and procedures should be developed for data acquisition and analysis to address the discrepancies encountered by digitization of data.

Neural network for grain yield predicting based multispectral satellite imagery: comparative study

Estimations of crop yield predictions are vital in the management of agronomical matters. Such Agronomical issues affecting agriculture include agricultural management, national food policies, as well as the international crop trade-which is under the mandate of Food agriculture organization (FAO). Also, an increase in food demand due to the ever-growing population has contributed to the cultivation of large tracts of land. Thus has led to the evolution of diverse methods as well as systems deployed for prediction of crop yield including the application of satellite images. Satellite techniques are utilized due to their capacity to continuously cover large areas while providing accurate estimations of crop yields. In this context of crop yield estimations, the vegetation indices provided by the satellite sensors, as well as land surface variables such as weather elements, soil moisture, hydrological conditions, soil fertility, and fertilizer application is used. Where the convenience of data acquisition and high prediction accuracy is mandatory, many empirical models based on machine learning techniques were employed and the most successful methodology applied was the neural network. The neural network data input varied in the form of normalized histograms of a multi-spectral image bands, normalized vegetation index, absorbed active photosynthetic radiation, canopy surface, and environmental factors. Our findings indicate that the rapid advances in satellite technologies and ML techniques will provide affordable and comprehensive solutions for accurate grain prediction. Many remote sensing researches for yield estimation is needed to adjust and develop the existing methods for more accurate grain crop prediction.

Research and Design of Distributed IoT Water Environment Monitoring System Based on LoRa

In order to solve the problems of high labor cost, long detection period, and low degree of information in current water environment monitoring, this paper proposes a lake water environment monitoring system based on LoRa and Internet of Things technology. The system realizes remote collection, data storage, dynamic monitoring, and pollution alarm for the distributed deployment of multisensor node information (water temperature, pH, turbidity, conductivity, and other water quality parameters). Moreover, the system uses STM32L151C8T6 microprocessor and multiple types of water quality sensors to collect water quality parameters in real time, and the data is packaged and sent to the LoRa gateway remotely by LoRa technology. Then, the gateway completes the bridging of LoRa link to IP link and forwards the water quality information to the Alibaba Cloud server. Finally, end users can realize the water quality control of monitored water area by monitoring management platform. The experimental results show that the system has a good performance in terms of real‐time data acquisition accuracy, data transmission reliability, and pollution alarm success rate. The average relative errors of water temperature, pH, turbidity, and conductivity are 0.31%, 0.28%, 3.96%, and 0.71%, respectively. In addition, the signal reception strength of the system within 2 km is better than ‐81 dBm, and the average packet loss rate is only 94%. In short, the system’s high accuracy, high reliability, and long distance characteristics meet the needs of large area water quality monitoring.

UAV-based partially sampling system for rapid NDVI mapping in the evaluation of rice nitrogen use efficiency

Rapid acquisition and mapping of normalized difference vegetation index (NDVI) values is essential for a timely evaluation of nitrogen use efficiency (NUE) in sustainable agriculture. The zonal nutrient management techniques for sustainable agriculture require timely and efficient NDVI data collection and analysis capabilities, – a need that is still rarely addressed. However, as with the use of manned aircraft, the primary limitation of established unmanned aerial vehicle (UAV) imaging technologies is the limited time window for large-area data collection. Furthermore, as the spatial resolution increases, the post-processing time generally becomes longer. To overcome problems associated with the need for rapid data acquisition and post-processing, we have developed a UAV-based sensor system called the Accurate and Speed Scanner (AS-Scanner). The data acquisition accuracy and NDVI mapping speed of the system were assessed. According to the results of a ground-level experiment, the mean absolute error (MAE) between the reflectance data of the AS-Scanner and the corresponding data of the ASD FieldSpec 3 spectrometer is less than 1.11%. Furthermore, results of a plot-level experiment suggest that the NDVI values of a raw point cloud from AS-Scanner-based UAV platform (AS-NDVI) and UAV-mounted MicaSense RedEdge camera (REG-NDVI) are highly correlated (0.54 < R2 < 0.57, p < 0.01). In a map-level experiment, the interpolated, 15 m × 15 m pixel size AS-NDVI values were in good agreement with the REG-NDVIs over a 4.01 ha (40100 m2) rice field (0.55 < R2 < 0.65, p < 0.01). The average processing times of the inverse distance weighted (IDW, 0.45 s), kriging linear (KL, 0.73 s), and nearest neighbor (NN, 1.05 s) interpolations of the AS-Scanner data prove that it is possible to efficiently create NDVI maps using data from the sensor. Besides, the effect of the varying solar irradiance values on the AS-NDVIs was analyzed under varied illumination conditions. Moreover, the AS-NDVIs were also used to assess the NUE in a paddy rice field at the panicle stage. The results show that nutrients can be effectively assessed using AS-Scanner data. Despite some disadvantages such as the potential of NDVI value saturation over dense canopies, the AS-Scanner provides a method for quickly obtaining reasonable spatial resolution crop information for agricultural nutrient monitoring and management. This also offers the potential to reduce environmental pressure which may be caused by untimely estimation of NUE.

Simulation and hardware implementation of demodulation for fiber optic seismic sensor with linear edge filtering method

Abstract The demodulation system is a very critical component of the seismic exploration, which determines the response speed and accuracy of data acquisition of the detection system. Here, we demonstrate a simulation and hardware implementation of demodulation system based on linear edge filtering method for fiber optic seismic sensor. The system is implemented mainly by an edge filter (long period fiber grating) with the linear slope of 1.368 dBm/nm and the sideband width of 6.05 nm, and a homemade circuit board. The results show that the system can be used to demodulate low-frequency (10 Hz–200 Hz) vibration signals, and the best signal-to-noise ratio is up to ~ 70 dB. Moreover, theoretically, the system could be used to demodulate higher frequency vibration and can be adapted to any vibration measurement, and suitable for engineering application due to the characteristics of simple structure, low cost, stable performance, fast response speed and high precision.

Improving data acquisition speed and accuracy in sport using neural networks

ABSTRACT Video analysis is used in sport to derive kinematic variables of interest but often relies on time-consuming tracking operations. The purpose of this study was to determine speed, accuracy and reliability of 2D body landmark digitisation by a neural network (NN), compared with manual digitisation, for the glide phase in swimming. Glide variables including glide factor; instantaneous hip angles, trunk inclines and horizontal velocities were selected as they influence performance and are susceptible to digitisation propagation error. The NN was “trained” on 400 frames of 2D glide video from a sample of eight elite swimmers. Four glide trials of another swimmer were used to test agreement between the NN and a manual operator for body marker position data of the knee, hip and shoulder, and the effect of digitisation on glide variables. The NN digitised body landmarks 233 times faster than the manual operator, with digitising root-mean-square-error of ~4-5 mm. High accuracy and reliability was found between body position and glide variable data between the two methods with relative error ≤5.4% and correlation coefficients >0.95 for all variables. NNs could be applied to greatly reduce the time of kinematic analysis in sports and facilitate rapid feedback of performance measures.

Emission Inventory and Characteristics of Anthropogenic Air Pollution Sources Based on Second Pollution Source Census Data in Sichuan Province

In this research, the activity data of Sichuan Province were collected using bottom-up and top-down methods. According to the second survey of pollution sources, the activity data of industrial source includes information of 11020 boilers and 60078 industrial enterprises. Data of 19152 industrial enterprises were collected in Chengdu, accounting for 32% of the total number of enterprises in Sichuan Province. The anthropogenic air pollutant emission inventory of 9 km×9 km was developed for Sichuan Province in 2017 with the use of appropriate emission estimation methods. The results showed that the total emission of SO2,NOx,CO,PM10,PM2.5,BC,OC,VOCs, and NH3 in Sichuan were 308.6×103, 725.7×103, 3131.2×103, 927.6×103, 422.4×103, 30.2×103, 72.0×103, 600.9×103, and 887.1×103 t. The fixed combustion source and process source mainly contributed as sources of SO2. The main source of CO was the process source and mobile source. Further, the dust source and process source were the main sources of PM10 and PM2.5, and the dust source was the largest source of BC and OC contributions. The emission sources of the VOCs were primarily the process sources, mobile sources and solvent use sources. The NH3 emissions were mainly from livestock and poultry breeding and nitrogen fertilizer applications. The spatial distribution results showed that the pollutants were mainly concentrated in the densely populated Sichuan basin and Panzhihua region, where industry and agriculture were relatively developed. The high value points are concentrated along the Deyang-Chengdu-Meishan-Leshan line in Chengdu Plain. The emission inventory established in this study still has certain uncertainties, and the accuracy of activity level data acquisition should be further enhanced. Moreover, pollutant emission factor testing should be carried out for typical pollution sources, and grid emission inventory should be improved to provide scientific support for the prevention and control of air pollution in Sichuan Province in the future.