Measurement Sensors最新文献

Myocardial Infarction otherwise called heart attack occurs in human beings when blood flow decreases or stops to a part of the heart which in turn damages the heart muscle. Prediction of abnormalities in cardio arrhythmia disease is done by using standard 12-lead Electrocardiography (ECG) signals, which also detects Posterior Myocardial Infarction (PMI). The QRS complex is the merged output of different parts of graphical deflection seen on a typical Electro Cardio Gram (Electrocardiography). The main purpose of the paper is to monitor and analyze particularly the R_peak upward deflections from the QRS complex. Denoising the ECG signal is done by butter worth filter. The denoised signals are used to detect R peaks and image plotting is done by segmentation. R peak images are used to classify the abnormalities in Myocardial Infarction (MI) with the help of the CNN image processing technique. The publicly available PTB diagnostic dataset is used to classify the abnormalities in PMI. The detection of the R peaks is used to guide Cardiologists must advance the Percutaneous Coronary Intervention treatment. Prediction has been done using probability weighted average method. Troponin level has been calculated to evaluate a person's health condition which also supports in close prediction of diseases and abnormalities. From experimental analysis of proposed Probability weighted average method in troponin level (PWAMT), the accuracy scores in the ensemble model were found to be 86 % respectively. The running of algorithm took 250 s–300 s to execute the program and display the prediction results.

In order to clarify the quantitative relationship between grid parameters and measurement errors of gateway energy meters, and accurately predict the dynamic measurement errors of gateway energy meters, the author proposes a nonlinear modeling of measurement errors of gateway energy meters. Firstly, elaborate on the NARX prediction model to clarify the basic structure of the nonlinear model; Then propose the process of modeling measurement errors; Finally, through testing, identify the main power grid parameters that affect measurement errors and the optimal structure of the model. The experimental results indicate that: The comparison between the true measurement error of two electricity meters and the measurement error calculated by the nonlinear estimator shows that the Hammerstein Weiner estimator has the highest fitting degree to the true measurement error curve, with fitting degrees of 82.21 % and 85.38 % for the measurement errors of 0.2S and 0.5S electricity meters, respectively. The prediction fit of the NRAX model based on the Hammerstein Weiner nonlinear estimator reaches about 81 % under different load conditions.

Conclusion

The model determined by this method can accurately predict the dynamic measurement error of the energy meter, and the research results have positive significance for improving the efficiency of gate energy meter calibration and identifying gate energy meter faults.

In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

In order to cope with the continuous development of information technology, the data volume of edge devices in the power supply network is increasing rapidly, and the higher requirements for real-time data processing and transmission bandwidth are put forward, the author proposes the research on security protection of multi-source data in smart grid based on edge computing. Established a distribution network safety risk map, calculated and analyzed safety risks through potential functions, and obtained node potential values and key node data; The intelligent grid multi-source heterogeneous data monitoring model based on edge computing can realize the timely perception and real-time response of distribution network faults, shorten the outage time, and improve the power supply reliability and user satisfaction of the distribution network. The results indicate that: Taking a distribution network line in a certain area as an example, the highest potential value is node 9, followed by nodes 10 and 3. This indicates that in order to ensure the reliable operation of the distribution network and avoid power outages, it is necessary to improve the repair capacity, the proportion of old equipment, and the power supply radius exceeding the standard.

Conclusion

The effectiveness of potential function in risk assessment of edge computing is verified, which can provide theoretical guidance for distribution network fault diagnosis.

Space research relies heavily on satellites, and technological developments are critical to the success of these missions. In order to estimate satellite pose, a simulation of an IMU sensor using Matplotlib is presented in this study. A roll, pitch, and yaw value analysis is performed on data obtained from the BNO-055 IMU sensor. A prototype ground station's helical antenna is used to investigate these parameters. For effective communication between the satellite body and ground station setup, a low-cost transceiver module RF-433Mhz module is connected to the ground station. IMUsensors are essential for inertial measurements in spacecraft, which support ground station monitoring. This study makes use of a four-turn helical antenna that is built for deployment and designed using the 4nec2 simulation program for RF-based satellite-ground station communication. The antenna's VSWR is 1.432, and its directivity is 18.581. Matplotlib is utilized to model variations in roll, pitch, and yaw values. Roll, pitch, and yaw ranges that are observed are −0.375 to 0.25, −0.375 to 0.5625, and −0.3125 to 0.1875, in that order. These ranges are useful in effectively visualizing the body pose of the CubeSat when connecting via PySerial to a Matplotlib-developed program.

In prestressed concrete (PSC) structures, corrosion in prestressing wire is one of the main problems affecting its service life. Therefore, structural health monitoring (SHM) is essential for performance evaluation and safety maintenance to prevent and reduce engineering accidents. In SHM, corrosion monitoring using the piezo sensor-based electro-mechanical impedance (EMI) technique has become a research hotspot. This paper presents the effectiveness of embedded and smart-probe-based piezo sensors (SPPS) for identifying structural parameters such as stiffness, mass, and damping in a prestressed concrete beam subjected to chloride-induced corrosion using electro-mechanical impedance (EMI) technique. The accelerated corrosion tests were conducted on a PSC beam in which SPPS was indirectly bonded, and an embedded piezo sensor (EPS) was attached to the prestressing wire inside the beam to monitor the variation in the EMI signature during the exposure of corrosion. Further, a physical model was developed in the form of spring, mass, and damper combinations to identify the deterioration in terms of structural parameters during exposure to corrosion. Based on the experimental results, it is found that EPS is effective in identifying the corrosion initiation phase, while SPPS is for the corrosion propagation and cracking phase. In terms of percentage loss, the identified stiffness loss from SPPS and EPS in the propagation phase of corrosion is about 61.56 % and 5 %, respectively.

Condition-based maintenance is very desirable for minimizing the maintenance and failure costs of power systems without sacrificing reliability. A systematic approach including an adaptive maintenance advisor and a system maintenance optimizer is proposed here for effectively handling the operational variations and uncertainties for condition-based maintenance. First, the maintenance advisor receives and implements the maintenance plans for its key components from the system maintenance optimizer, which optimizes the maintenance schedules with multi-objective evolutionary algorithm, considering only major system variables and the overall system performance. During operation, the offshore substation will experience continuing ageing and shifts in control, weather and load factors, measurement and human judgment detected from the connected grid and all other equipments with uncertainties. Then, the advisor estimates the changes of reliability indices due to operational variations and uncertainties of its key components by hierarchical fuzzy logic and sends the changes back to the maintenance optimizer. The maintenance optimizer will upgrade the load-point reliability and report any drastic deterioration of reliability within each substation, which may lead to re-optimization of the substation's maintenance activities for meeting its desired reliability. The offshore substation connected to a medium-sized onshore grid will be studied here to demonstrate the ability of this proposed approach in dealing with uncertainties in the implementation of maintenance with significant reduction of computational complexity and rule base.

Conventional works carried out in Wireless Sensor Networks (WSN) mostly focussed on energy oriented services and very less significant measures given to delay oriented and congestion aware services. Hence the proposed mechanism specially focuses on network structural design by placing rendezvous location for each cluster as well as route segmentation for controlling the congestion occurrence and unwanted delay. Here Congestion Aware Clustering with Improved Ant Colony Algorithm (CAC_IACA) is proposed. This mechanism involves two steps (i) identifying the best route by following the Ant Colony Optimization (ACO) algorithm and (ii) data segmentation using rendezvous mobile nodes. The Rendezvous nodes are present in each cluster to reduce the congestion rate on receiver side during data transmission. This proposed methodology mainly concentrates on reducing coverage cost for 3D environmental monitoring. Simulation results are analysed and the efficiency of the proposed scheme proves 26.54 % better than the conventional method.

Energy dissipation is the most important design limitation for Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). In order to prolong the life of WSNs, the energy of nodes must be used in an effective way. Clustering is a strategy that may effectively use the energy of the sensors, extending the life and scalability by managing the network load balance. The energy usage for network operation is reduced by using an evolutionary algorithm called Genetic Algorithm (GA). The Stochastic Cluster Head Selection Model (SCHSM) is described in the proposed protocol by taking the factors such as distance, node energy, density and capacity of nodes for developing the fitness function. The proposed protocol is designed for multiple movable sink nodes and this greatly improves the energy balancing factor in the network. For minimizing the communication gap among sensors and sinks, movable sinks can be placed carefully. Simulation results are analyzed for the system effectiveness.