Research on Pump Inspection Cycle Early Warning Method Based on Big Data

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.

Inspection Threshold for Multistate System

Determination of inspection period for a multistate system with imperfect inspection is an important issue for maintenance management. The result of imperfect inspection has potential to lead wrong information. This wrong information can cause bad decisions. How to eliminate the imperfect inspections is an important problem. This paper considers an inspection threshold which eliminates unnecessary imperfect inspection for the multistate system. In particular, it is intended for a system in which the number of unit failures is defined as the system state.

Efektivitas Pemeriksaan, Penagihan Pajak dengan Surat Paksa dalam Rangka Meningkatkan Penerimaan PPN Pada KPP Pratama Banjarmasin

The purpose of this study to analyze the effectiveness of the examination, collection with forced letters to increase VAT receipts at KPP Pratama Banjarmasin from 2013-2017.This study uses a descriptive method describing the data processed and analyzed qualitatively based on realization data and comparison targets, with a standard reference of the specified indicators.The results of the analysis of the implementation of VAT audits at KPP Pratama Banjarmasin in 2013, 2015 and 2016 were less effective. 2014 is quite effective and 2017 has been effective. Some of the issuance of the SP2 is issued at the end of the year and may not be completed in the year concerned. The extent of the examination and data on taxpayers have not been maximally fulfilled so that they require a long inspection period. The implementation of tax collection with a forced letter of value-added in 2013-2016 was dominated by ineffective indicators both in terms of the number of forced letters submitted and the nominal amount. Based on the forced letters issued in that year all can not be conveyed by the bailiff due to difficulty identifying or finding the address of the taxpayer and it is not yet clear. The effectiveness of 2017 in terms of absolute shows very effective criteria. The tax authorities carry out active collection of forced letters issued in 2017. Then new regulations PMK 165 / PMK.03 / 2017 are issued regarding tax arrears or calculation of unpaid tax payable at the prescribed rates. Then the taxpayer is more cooperative in paying his tax debt.

An Inspection Period Analysis of the Grinder Rods Eccentric Wear of the Rod-Pumped well Based on the Multi-Linear Regression Equation

This paper aims at establishing the empirical equations of the qualitative relationship between the inspection periods of the grinder rods eccentric wear of the rod-pumped well and its influencing factors and putting a hypothetical test of it to find out the scientific evidences for the solutions to the eccentric wear wells on the basis of multi-linear regression equation.

Modelling the spread of American foulbrood in honeybees

We investigate the spread of American foulbrood (AFB), a disease caused by the bacterium Paenibacillus larvae , that affects bees and can be extremely damaging to beehives. Our dataset comes from an inspection period carried out during an AFB epidemic of honeybee colonies on the island of Jersey during the summer of 2010. The data include the number of hives of honeybees, location and owner of honeybee apiaries across the island. We use a spatial SIR model with an underlying owner network to simulate the epidemic and characterize the epidemic using a Markov chain Monte Carlo (MCMC) scheme to determine model parameters and infection times (including undetected ‘occult’ infections). Likely methods of infection spread can be inferred from the analysis, with both distance- and owner-based transmissions being found to contribute to the spread of AFB. The results of the MCMC are corroborated by simulating the epidemic using a stochastic SIR model, resulting in aggregate levels of infection that are comparable to the data. We use this stochastic SIR model to simulate the impact of different control strategies on controlling the epidemic. It is found that earlier inspections result in smaller epidemics and a higher likelihood of AFB extinction.