Data compression algorithms for sensor networks with periodic transmission schemes

The operating state of switch cabinet is significant for the reliability of the whole power system, collecting and monitoring its data through the wireless sensor network is an effective method to avoid accidents. This paper proposes a data compression method based on periodic transmission model under the condition of limited energy consumption and memory space resources in the complex environment of switch cabinet sensor networks. Then, the proposed method is rigorously and intuitively shown by theoretical derivation and algorithm flow chart. Finally, numerical simulations are carried out and compared with the original data. The comparisons of compression ratio and error results indicate that the improved algorithm has a better effect on the periodic sensing data with interference and can make sure the change trend of data by making certain timing sequence.

RPA and Artificial Intelligence in Budget Management Based on Multiperspective Recognition Based on Network Communication Integration

Nowadays, RPA robots are increasingly used in daily office tasks such as finance and human resources. They play an increasingly important role in realizing office automation, which can improve work efficiency and reduce labor costs. In order to improve the efficiency of budget management and save human resources, this paper conducts related research based on the multiview recognition technology of network communication integration, combined with RPA in artificial intelligence technology. In the method part, this article introduces the mode of network communication integration and the principles that should be followed, as well as the related processes of RPA. In the algorithm, this paper introduces an integrated algorithm based on ELM. In the experimental part, this article predicts the performance of each model, compares identification functions with different signal-to-voice signals, and compares timing functions on different signal-to-voice signals, periodic transmission mode indicators, recognition rates of different kernel functions, and comparison of average recognition rates and multiview recognition rate comprehensive analysis of these multiple aspects. Under the same conditions, the recognition rate of some angles is lower than other angles; 0 degrees, 18 degrees, 126 degrees, and 180 degrees are slightly lower than other angles, which will affect the average recognition rate of the entire recognition. But for multiview gait features, considering the influence of each angle on the recognition rate, the characteristics of each angle are merged together, so that the recognition rate is significantly higher than the average recognition rate of 11 angles. It can be seen that multiview recognition based on network communication integration does have obvious effects on RPA and artificial intelligence in budget management and can improve the efficiency of budget management. The multiperspective recognition technology designed in this study can realize modernization and digitization in budget management.

Simulation and prediction of dengue outbreaks based on an SIR model with a time-dependent transmission rate including meteorological data. An example for Colombo and Jakarta

Vector-borne diseases can usually be examined with a vector–host model like the [Formula: see text] model. This, however, depends on parameters that contain detailed information about the mosquito population that we usually do not know. For this reason, in this article, we reduce the [Formula: see text] model to an [Formula: see text] model with a time-dependent and periodic transmission rate [Formula: see text]. Since the living conditions of the mosquitos depend on the local weather conditions, meteorological data sets flow into the model in order to achieve a more realistic behavior. The developed [Formula: see text] model is adapted to existing data sets of hospitalized dengue cases in Jakarta (Indonesia) and Colombo (Sri Lanka) using numerical optimization based on Pontryagin’s maximum principle. A previous data analysis shows that the results of this parameter fit are within a realistic range and thus allow further investigations. Based on this, various simulations are carried out and the prediction quality of the model is examined.

A Model-Based SHM Strategy for Gears—Development of a Hybrid FEM-Analytical Approach to Investigate the Effects of Surface Fatigue on the Vibrational Spectra of a Back-to-Back Test Rig

Transmissions are extensively employed in mechanical gearboxes when power conversion is required. Being able to provide specific maintenance is a crucial factor for both economics and reliability. However, although periodic transmission maintenance increases the systems’ longevity, it cannot prevent or predict sporadic major failures. In this context, structural health monitoring (SHM) represents a possible solution. Identifying variations of a specific measurable signal and correlating them with the type of damage or its location and severity may help assess the component condition and establish the need for maintenance operation. However, the collection of sufficient experimental examples for damage identification may be not convenient for big gearboxes, for which destructive experiments are too expensive, thus paving the way to model-based approaches, based on a numerical estimation of damage-related features. In this work, an SHM approach was developed based on signals from numerical simulations. To validate the approach with experimental measurements, a back-to-back test rig was used as a reference. Several types and severities of damages were simulated with an innovative hybrid analytical–numerical approach that allowed a significant reduction of the computational effort. The vibrational spectra that characterized the different damage conditions were processed through artificial neural networks (ANN) trained with numerical data and used to predict the presence, location, and severity of the damage.

Modeling, Control, and Prediction of the Spread of COVID-19 Using Compartmental, Logistic, and Gauss Models: A Case Study in Iraq and Egypt

In this paper, we study and investigate the spread of the coronavirus disease 2019 (COVID-19) in Iraq and Egypt by using compartmental, logistic regression, and Gaussian models. We developed a generalized SEIR model for the spread of COVID-19, taking into account mildly and symptomatically infected individuals. The logistic and Gaussian models were utilized to forecast and predict the numbers of confirmed cases in both countries. We estimated the parameters that best fit the incidence data. The results provide discouraging forecasts for Iraq from 22 February to 8 October 2020 and for Egypt from 15 February to 8 October 2020. To provide a forecast of the spread of COVID-19 in Iraq, we present various simulation scenarios for the expected peak and its timing using Gaussian and logistic regression models, where the predicted cases showed a reasonable agreement with the officially reported cases. We apply our compartmental model with a time-periodic transmission rate to predict the possible start of the second wave of the COVID-19 epidemic in Egypt and the possible control measures. Our sensitivity analyses of the basic reproduction number allow us to conclude that the most effective way to prevent COVID-19 cases is by decreasing the transmission rate. The findings of this study could therefore assist Iraqi and Egyptian officials to intervene with the appropriate safety measures to cope with the increase of COVID-19 cases.

Negative Curvature Hollow Core Fiber Based All-Fiber Interferometer and Its Sensing Applications to Temperature and Strain

Negative curvature hollow core fiber (NCHCF) is a promising candidate for sensing applications; however, research on NCHCF based fiber sensors starts only in the recent two years. In this work, an all-fiber interferometer based on an NCHCF structure is proposed for the first time. The interferometer was fabricated by simple fusion splicing of a short section of an NCHCF between two singlemode fibers (SMFs). Both simulation and experimental results show that multiple modes and modal interferences are excited within the NCHCF structure. Periodic transmission dips with high spectral extinction ratio (up to 30 dB) and wide free spectral range (FSR) are produced, which is mainly introduced by the modes coupling between HE11 and HE12. A small portion of light guiding by means of Anti-resonant reflecting optical waveguide (ARROW) mechanism is also observed. The transmission dips, resulting from multimode interferences (MMI) and ARROW effect have a big difference in sensitivities to strain and temperature, thus making it possible to monitor these two parameters with a single sensor head by using a characteristic matrix approach. In addition, the proposed sensor structure is experimentally proven to have a good reproducibility.