Data Acquisition Equipment Research Articles

Development of a Lower Limb Finite Element Musculoskeletal Gait Simulation Framework Driven Solely by Inertial Measurement Unit Sensors

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have a major disadvantage that makes them challenging to apply in clinical environments, i.e., they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. The target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics.

High-energy gamma-rays and neutrons observed in very impulsive solar flare on 2012 June 3

AbstractAn impulsive solar flare was observed on 2012 June 3 by the Geostationary Operational Environment Satellite. In this flare, a salient feature was recognized whereby high-energy ions were accelerated to a few GeV within one minute. Associated with this flare, high-energy gamma-rays as well as high-energy neutrons were detected by the Space Environment Data Acquisition equipment onboard the International Space Station. A remarkable feature found of this event was an exceedingly high n$/$γ-ratio observed between the intensities of high-energy neutrons and gamma-rays. To understand a high n$/$γ-ratio of about 600, the interaction processes between the accelerated ions and ambient solar atmosphere were simulated for various test cases, taking into account the magnetic field of the photosphere. The simulation was extended by using the GEANT4 code and it was shown that the observed results could be reproduced under certain conditions selected for the actual interaction process. The results suggest that helium and C, N, and O ions were predominantly accelerated in this flare, and that high-energy gamma-rays and neutrons were produced by interactions between the accelerated ions and the ambient gas. As a result, a high n$/$γ-ratio was produced.

The glial framework reveals white matter fiber architecture in human and primate brains.

Uncovering the architecture of white matter axons is fundamental to the study of brain networks. We developed a method for quantifying axonal orientations at a resolution of ~15 micrometers. This method is based on the common Nissl staining technique for postmortem histological slices. Nissl staining reveals the spatial organization of glial cells along axons. Using structure tensor analysis, we leveraged this patterned organization to uncover local axonal orientation. We used Nissl-based structure tensor analysis to extract fine details of axonal architecture and demonstrated its applicability in multiple datasets of humans and nonhuman primates. Nissl-based structure tensor analysis can be used to compare fine-grained features of axonal architecture across species and is widely applicable to existing datasets.

On-Orbit Pile-Up Detection and Digital Pulse Shape Measurement Results in the Radiation Telescope

We have developed a radiation environment monitor called the Technical Data Acquisition Equipment (TEDA) installed in the RAPid Innovative payload demonstration Satellite 1 (RAPIS-1) which was launched on January 18, 2019. The TEDA measured the energy spectra of electrons, protons, and He ions. In addition to the functions of a conventional sensor, we added a fast digital sampling function to the TEDA. The operational verification in orbit was successful, and the pile-up waveform signal was acquired for the first time at The Japan Aerospace Exploration Agency (JAXA). The TEDA also obtained data showing that pile-up detection also worked properly.

Calibrated Phasor Measurement Unit as a Reliable Metrological Tool for National Power Grid Operation in India

At the national power grid in India, stability is one of the most important factors due to disturbances caused by distributed load and time-variant sources. Presently, for monitoring the transmission efficiency and performance of power grids, phasor measurement units (PMUs) are being installed at various locations in the country. Time synchronization, using Coordinated Universal Time (UTC), makes PMU an important and reliable data acquisition equipment across the grids. To ensure reliability and accuracy of the acquired data, PMUs must be calibrated. However, recent development of automated PMU calibrator system by NIST and M/s Fluke, USA has revolutionized the calibration process by enhancing the accuracy and consistency of PMU measurements. The CSIR-NPL PMU system is fully operational to calibrate PMUs according to the IEEE synchrophasor standards. The time consumed to perform the PMU calibration is comparatively much less than the conventional method. A traceable PMU calibrator system has great potential in calibrating PMUs used to monitor, control and protect the power grid.

Computer wireless network integration technology in equipment state data collection

The paper introduces a handheld integrated power data acquisition and analysis equipment based on a computer wireless network mobile platform, an intelligent transportation inspection box. The intelligent transportation inspection box acts as a bridge between the transportation inspection work site side and the power grid company center to realize data and resource sharing between the site and center sides. The application of intelligent operation and inspection boxes based on computer wireless network integration technology is essential for improving the professional and intelligent level of on-site operation and maintenance, improving the efficiency of operation and inspection work and the ability to control equipment status, strengthening state maintenance and auxiliary decision-making, and promoting the construction of smart grids significance.

Research on Classification Method of 3D Point Cloud Model Based on KE-PointVNet

With the popularization and use of three-dimensional data acquisition equipment, the acquisition of three-dimensional data is more convenient. Three-dimensional data contains rich shape and scale information. How to effectively and accurately classify, segment and identify three-dimensional data is a research hotspot in the field of computer vision. Aiming at the particularity of 3D point cloud data and the neglect of local correlation between points, this paper proposes a new end-to-end depth network framework, namely KE-PointVNet. KE-PointVNet can directly deal with point cloud and construct deep convolutional network. KE-PointVNet extracts local geometric features of each point based on the EdgeConv module. By using the KNN proximity algorithm to construct local neighborhood of the point cloud, it can avoid its non-local diffusion, and finally obtain the high-level semantics of the point cloud through the local aggregation vector VLAD layer. The experimental results show that this method has a higher classification accuracy than most of the existing point cloud classification methods.

A Landslide Monitoring System for Natural Terrain in Korea: Development and Application in Hazard Evaluations.

This study describes the development of a landslide monitoring system for the purpose of reducing damages caused by landslides in natural terrain. The system was developed to analyze the effects of landslide-inducing rainfall and the behavior of slopes through 12 monitoring stations that are distributed across eight national parks in Korea. Several sensors and a data acquisition equipment to monitor landslide were installed in each station. The composition of the system and its operating program were designed to efficiently manage the sizeable amounts of real-time monitoring data that are collected from the various stations. To test the potential of the developed system for reliable landslide hazard evaluations, data measured over a five-year period by the two monitoring stations in Jirisan National Park were analyzed. Subsequently, the suction stress of the soil over the monitoring period was calculated by applying laboratory test result of the geotechnical and unsaturated soil properties in the analysis domain area. The infinite slope stability analysis combined with an effective stress concept based on the suction stress was applied to calculate the factor of safety. This method also enabled the temporal and quantitative evaluation of slope stability in natural terrain. In addition, based on the monitoring and slope stability analysis results, an analysis for the spatial classification of landslide hazards was conducted. The analysis results quantitatively and statistically demonstrated that 98% of historical landslide initiation areas were classified as high hazard levels.

Spatial and Temporal Pattern Analysis of Land Use in Yangtze River Delta Based on Remote Sensing and GIS in Intelligent Environment

Land use change plays an important role in regional socio-economic development and global environmental change. Whether the land is effectively and efficiently used is not only related to the income level of the people in the surrounding cities but also closely related to the local economy and national economy. Intelligent environment refers to the indoor environment with a variety of data acquisition equipment. Combined with related technologies, the reasoning and analysis of the data can be used to realize the functions of activity identification, data perception, and control. In addition, the Yangtze River Delta is an economically developed area in China, and its land use situation is related to the economic development in the next ten or even decades. Therefore, it is necessary to analyze the spatial and temporal pattern of land use in Yangtze River Delta region by remote sensing image technology and GIS in intelligent environment. Based on intelligent environment, this paper uses RS and GIS technology to interpret remote sensing image and map land use in multitemporal coastal zone. The land use dynamic degree model and spatial interpolation method were used to analyze and evaluate the spatial and temporal evolution characteristics of land use in the Yangtze River Delta region, and the landscape pattern changes in the Yangtze River Delta region were analyzed and evaluated. This study found that the land use types in the Yangtze River Delta have transformed each other, and the land use change speed is fast, which is inseparable from the rapid economic development. In the future, in addition to maintaining the rapid and stable development of industry, the rational use of limited land resources, the improvement of agricultural development short board, and the improvement of tourism economic benefits will make the economy of the Yangtze River Delta region to a new level.

The application on the towed-type shallow high-accuracy seismic detection for coastal tidal flats

Restricted by the geological exploration equipment and tidal time, the deep seismic detection technology with large offset for exploration of oil and gas resources is often used in the seismic survey of the coastal tidal flats. Furthermore, the coastal tidal flats are only treated as the transition belt of the paralic zone, resulting in the low degree of geological exploration and low detection accuracy, which cannot meet the requirement for high-accuracy exploration in the coastal tidal flats. Conventional shallow seismic exploration technology can detect effectively the bottom interface and internal horizon of the shallow Quaternary system in the coastal tidal flats, but the equipment for data acquisition is complex, and the acquisition efficiency is low, restricting directly the seismic exploration in the coastal tidal flats. Therefore, a shallow high-accuracy seismic detection technology has been developed in this study, which is suitable for the coastal tidal flats. By making full use of the characteristics of the flat terrain in the coastal tidal flats, the method for towed data acquisition has been used, and the seismic detection equipment has been simplified in the tidal flats. With this method, the exploration cost has been reduced, and the data acquisition efficiency has been improved. The detection accuracy can reach the level of meters, and the detection depth is more than 800m with this method. With this technology, the shallow stratigraphic structure and interface in the coastal tidal flats can be detected effectively, and the location of paleochannels, concealed faults and shallow gas can be determined, which support effectively the geological survey of the coastal zone and offshore engineering, being of great practical significance for scientific disaster prevention and mitigation.

Algorithms for the selection of fluorescent reporters

Molecular biologists rely on the use of fluorescent probes to take measurements of their model systems. These fluorophores fall into various classes (e.g. fluorescent dyes, fluorescent proteins, etc.), but they all share some general properties (such as excitation and emission spectra, brightness) and require similar equipment for data acquisition. Selecting an ideal set of fluorophores for a particular measurement technology or vice versa is a multidimensional problem that is difficult to solve with ad hoc methods due to the enormous solution space of possible fluorophore panels. Choosing sub-optimal fluorophore panels can result in unreliable or erroneous measurements of biochemical properties in model systems. Here, we describe a set of algorithms, implemented in an open-source software tool, for solving these problems efficiently to arrive at fluorophore panels optimized for maximal signal and minimal bleed-through.

A physical modeling-based study on the control mechanisms of Negative Poisson's ratio anchor cable on the stratified toppling deformation of anti-inclined slopes

With ongoing increases in excavation depth, the large-scale toppling instability failure of stratified anti-inclined slopes is gaining wide attention. To address the problem of controlling toppling deformation failure of anti-inclined slopes with traditional small-deformation materials, this paper takes the results of existing studies on the extraordinary mechanical properties of engineering-scale Negative Poisson's ratio (NPR) anchor cable as a theoretical basis, and develops a model-scale NPR anchor cable according to similarity theory. Relying on a self-developed “engineering disaster model experimental system”, a physical modeling-based experiment to explore the reinforcement mechanism of stratified anti-inclined slope using model-scale NPR and ordinary anchor cables is performed. The physical model is monitored using static strain data acquisition equipment, an infrared thermal imager, tension sensors, and digital speckle correlation method (DSCM) displacement field measurement equipment. The evolution of the displacement field, strain field, temperature field, and anchor cable force are obtained during excavation on the physical model. By comparing the evolution of these parameters with images from both the anti-inclined slope model instability failure test and the deformation characteristics on the two sides of the slope, which were reinforced using different types of anchor cables, this paper determines the mechanisms governing instability failure of anti-inclined slopes reinforced with NPR anchor cable. In addition, this paper also proves that NPR anchor cable can be used to monitor the sliding force of anti-inclined slopes throughout excavation, and lays a foundation for the application of NPR anchor cable monitoring technology to the advanced anti-inclined slope failure warning.

Research on Intelligent Monitoring and Early Warning of Electric Power Safety Based on Artificial Intelligence Technology

In recent years, with the continuous improvement of the national economic development level, the scale of urban construction in China is also gradually expanding. Therefore, since the monitoring system plays an important role in various industries, the establishment of an intelligent automatic management system can determine the unstable factors in the monitoring management and improve the safety performance of the monitoring system, so that the normal operation of the monitoring platform can be maintained. The establishment and function of the intelligent monitoring and early warning system are analyzed and studied in the paper, which combines traditional video monitoring equipment and data acquisition equipment into one, and launches a monitoring and early warning solution.

Traffic forecasting and road management based on graph neural networks and big data

With the continuous development of China's society and economy, urban traffic problems have become increasingly serious. Analyzing the data gnerated by the city will help us better solve the urban traffic problem. This paper defines a key road in the city, and installs data acquisition equipment on these roads. By understanding the traffic conditions of key roads, we can understand the traffic conditions of other roads in the city. In this way, only a small number of roads need to be monitored in the city, which can reduce the workload of urban data analysis, greatly improve the efficiency of data analysis and reduce the cost of urban construction. This paper combines the common traffic flow prediction scenarios in traffic problems to verify the importance of these key roads. The GPS data of taxis in Changchun City are mainly used for statistical analysis of urban road traffic flow. The main work includes the following aspects: [1] Firstly, the parallel processing method is used to match the taxi GPS data, which greatly improves the matching efficiency. The POI interest point data, road network data and ground induction coil data of Changchun City are crawled. The road flow data is obtained according to GPS data statistics. Grid the city. Road matching is carried out on the ground induction coil data. It is considered that the roads with ground induction coils are the key roads selected by human experience, and these roads are defined as the initialized key roads. Secondly, using the road network map data, the embedded coding features of the road are obtained through the graph embedding series models in the graph neural network method. The embedded features and road attributes are used to cluster the roads, and the key roads based on graph neural network are selected according to the clustering results. Then, the road flow prediction model is mainly divided into full data model and sparse data model. The full data mode uses all road history data, and the sparse data mode uses key road history data. The feature engineering of sparse data model is constructed, which mainly includes five parts: Road inherent attribute characteristics, road network diagram relationship characteristics, POI interest point characteristics, graph embedding characteristics and key road flow characteristics. A variety of machine learning and deep learning methods are tried to construct the road flow prediction model. Finally, the total cost evaluation standard of key road selection results is defined, which is composed of time cost, data cost and precision cost. [2]The time cost is the time taken for model training, the data cost is the proportion of the number of key roads to the total number of roads, and the accuracy cost is the accuracy of the road flow prediction model. The total cost experimental results of full data mode and sparse data mode are compared to verify the feasibility of sparse data mode in display scene. Comparing the key roads of initialization mode selection with the key roads of graph neural network mode selection, it is verified that the key roads of graph neural network mode selection have lower total cost, and can reduce or optimize the existing key roads in the existing cities, so as to achieve the purpose of reducing the cost of urban construction. This paper establishes a traffic prediction model to calculate the flow of other urban roads according to the sparse data of key roads. By reducing the number of monitoring equipment to reduce the cost of urban construction, we can also calculate the flow information of roads without monitoring equipment in the past. Since only road network map data and road attribute data are required for the selection of key roads, this method can also be used for auxiliary design in the planning of new urban roads, so as to reduce the workload and avoid the error of manual operation.

A small-sample faulty line detection method based on generative adversarial networks

This paper presents a small-sample faulty line detection method for a small current grounded system (SCGS) using improved generative adversarial networks (GANs) to overcome the problems of low accuracy and poor economy associated with traditional methods. First, we collected real-time fault data of the dispatching system as the original sample and used the linear interpolation and normalization methods to preprocess the data. We extracted and labeled the features of the original sample to construct a feature vector that represents the single-phase grounded fault. Then, to solve the problem of insufficient faulty samples, this paper used the GAN to generate synthetic samples to expand the training sample size. To stabilize the GAN training, the Wasserstein distance was introduced to improve the GAN loss function (WGAN). Finally, for the synthetic samples, we used Batch Normalization (BN) and an Early Stopping algorithm to train the lightweight depthwise separable convolutional network (DSCN) classifier and obtain a faulty line detection model with fast convergence and high accuracy. With the data of an actual dispatching system in China as an example, the proposed method determined the faulty line within 100–200 ms with an accuracy of more than 96%, which is approximately 26% higher than the accuracy of traditional methods. Compared with mainstream machine learning algorithms, the proposed method realizes the self-extraction of faulty features and simplifies the algorithm. Moreover, the accuracy of the proposed method is much higher than that of other machine learning algorithms on small samples. It is worth noting that this method needs only the existing real-time dispatching data and does not need to install additional data acquisition and fault detection equipment, eliminating the complicated equipment installation and debugging process and greatly reducing equipment costs human operations and maintenance costs. The proposed method has been implemented in an actual power system in China for two years. The results show that it is highly accurate and cost-effective, performs well in real time, and has bright prospects for future applications.

Application and Analysis of Big Data Technology in Smart Grid

In order to make the modern power system more intelligent, various data acquisition equipment and information management systems are required, such as smart meters, remote terminal units (RTU), phasor measurement units(PMU), power distribution management systems(DMS), and energy management systems(EMS), customer management system(CMS), power generation management system(GMS) have been widely installed. These systems create large amounts of data, which is the main source of smart grid big data. Researching advanced data analysis technology is the most urgent task for the development of smart grid. It can make the best decision for the control and operation of the grid, and can improve the flexibility, safety, reliability and efficiency of the power system. This article analyses the cause of smart grid big data and the importance of data analysis, and discusses the development trend of big data technology. Selectively analysed the source and type of smart grid big data, summarized the characteristics of smart grid big data, gave various application scenarios, and introduced the smart grid big data analysis platform and the functions being developed.

Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

In order to remedy problems encompassing large-scale data being collected by photovoltaic (PV) stations, multiple dimensions of power prediction mode input, noise, slow model convergence speed, and poor precision, a power prediction model that combines the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) with Long Short-Term Memory (LSTM) network was proposed in this study. The corresponding model uses factor correlation coefficient to evaluate the factors that affect PV generation and obtains the most critical factor of PV generation. Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. The training speed and convergence speed of LSTM are improved by the dimension-reduced data. The PV generation data of a certain power station over a period is collected from SolarGIS as sample data. The model is compared with Markov chain power generation prediction model and GA-BP power generation prediction model. The experimental results indicate that the generation prediction error of the model is less than 3%.

Investigating Toppling Failure Mechanism of Anti-dip Layered Slope due to Excavation by Physical Modelling

The failure mechanism of anti-dip layered slopes is essentially different from that of dip layered slopes. Therefore, it is important to investigate the failure mechanism of anti-dip slopes due to excavations. In this study, slope instability induced by mining excavation at the Changshanhao open-pit mine in Neimenggu province, China, was used as a case study. Based on the similarity ratio theory, a physical model was built to investigate the failure mechanism of the anti-dip layered slope under excavation. The physical model was monitored by various monitoring equipment including static strain data acquisition equipment, infrared thermal camera, and digital speckle displacement field measurement equipment. The evolution characteristics of the multi-physics fields including displacement field, strain field and temperature field of the physical model during the excavation were comprehensively obtained. According to the deformation characteristics of the anti-dip layered slope during excavation test, the failure mechanism can be divided into four stages: initial compression stage, crack generation stage, crack propagation stage and formation of sliding surface stage. The deformation characteristics of the slope at each stage were analyzed and compared with those of the anti-dip slope in the field. The comparison verified the rationality and accuracy of the physical model experiment, and provided a deeper understanding of the failure mechanism of anti-dip layered slope under excavation through the comprehensive monitoring data. The results of this work can be used as a reference for the follow-up reinforcement and treatment of similar anti-dip layered slopes.

An Optimized Pulse-Counting Method for Compensation Vector Calculation in the Automatic Balancer

The accuracy of the counterweight positions in an automatic balancing system deeply affects dynamic balancing. Compensation vector is synthesized by the two counterweights located in the electromagnetic dual-weight automatic balancer. Therefore, if the position of the counterweight is inaccurate, it may result in a wrong adjustment and a larger imbalance of the rotor system. In this paper, an optimized pulse counting method for compensation vector calculation in an electromagnetic dual-weight balancing system is proposed based on a programmable logic controller (PLC). A propeller automatic balancing simulation test bench is used to verify the effect of the method by obtaining the positions of the counterweights and synthesizing the compensation vector in the working mode. The error is less than 1/80 which means that it does not exceed one step in the 80-position-balancer at 1200 rpm. The proposed control system can work without computers or high-speed data acquisition equipment, which improves the stability and flexibility of the control system, facilitates the design of the automatic balancing system, and shows excellent potential for industrial applications.

Overview about cryogenic distillation control and safety approach for Isotopes Separation Facility (ISF)

Abstract Isotopes separation technologies are used in both Tritium Removal Facilities associated with CANDU reactors (to remove tritium from heavy water) and fusion applications (eq. ITER Isotope Separation System) to recover and enrich tritium for further operation. The expertise gained during design, manufacturing, building, commissioning and operation of the Cryogenic Distillation System (CDS) with proprietary four column design built at ICSI Rm. Valcea, could be applied for various tritium recovery applications, regardless of the type of front-end process used, Liquid Phase Catalytic Exchange or Combined Electrolysis and Catalytic Exchange. Cryogenic Distillation feed is a mixture of hydrogen isotopes which presents hazards in operation (explosion and tritium contamination), therefore the CDS of PESTD was designed to meet industrial and nuclear applicable standards. In this paper we present an overview of the CDS focusing on the process control and safety approach which meet specific conditions of Romanian codes and standards required for operation. However, specific information may be used later as reference for design consideration for other CDS. The cryogenic distillation process from ICSI Rm. Valcea is designed as four columns cascade inside a cold-box which acts as second barrier against hydrogen release and thermal insulation. The CDS process control is based on a Programmable Automation Controller (PACs), a Supervisory Control and Data Acquisition (SCADA) and specific low temperature field equipment and devices. The control room operator supervises the process using a specific designed Human Machine Interface (HMI) which permits to monitor process parameters, alarms acknowledgment and acts to adjust the process or override normal PACs control.