Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

Evaluation of ethanol preconditioning as a rapid seed vigour and viability test

It is essential that tests for evaluating seed vigour be faster and increasingly efficient to enable precise differentiation among batches. In this way, it is possible to evaluate the quality of seeds based on the anaerobic metabolism of cells when exposed to environments lacking oxygen. Thus, the objective of this study was to establish methodology for evaluating the viability and vigour of 3 lots of cowpea (Amendoim cultivar) seeds using the ethanol test. The treatments were carried out in a completely randomized design with four replications. For the test, 25 seeds were stored in hermetically sealed PET (Polyethylene the Ethylene) bottles containing 40 ml of distilled water and subjected to 3 soaking times in distilled water (6, 24, and 48 h) at a controlled temperature of 40°C in a germinator. The amount of ethanol produced was quantified with the aid of an adapted breath analyser. The results are expressed as mg L-1; these data were then compared with data for the following: germination; the first germination count; the total length, root length and shoot length of the seedling; dry weight of the seedling; emergence in the field; emergence speed index; and electrical conductivity. The experimental design was completely randomised, and the data were subjected to analysis of variance and correlation analysis. The results were compared using the Tukey test at 5% probability. Measurement of ethanol after 6 or 48 h of soaking at 40°C was effective for determining the viability and vigour of cowpea seeds. As ethanol test results have high correlation with germination and vigour test results, this approach is a viable alternative for analysts and seed producers.

Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System

The walking, waiting, transfer, and delayed in-vehicle travel times mainly contribute to route’s travel time reliability in the metro system. The automatic fare collection (AFC) system provides huge amounts of smart card records which can be used to estimate all these times distributions. A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. Finally, based on AFC data in Guangzhou Metro, the estimations show that all parameters can be estimated endogenously and identifiably. Meanwhile, the truncated property of the travel time is significant and the threshold tested by the surveyed data is reliable. Furthermore, the superiority of the proposed model over the existing model in estimation and forecasting accuracy is also demonstrated.