Penentuan Paket Promosi Pakaian PT. D&C Production dengan Menggunakan Algoritma FP-Growth

PT. D&C Production is a center for selling, foam mattresses, mattresses of all sizes and models, may types of car accessories and various kinds of women's and men's underwear. Problems regarding the decline in sales resulted in the accumulation of goods so that it became a loss. Data mining can be a solution to overcome these problems. This study will use the fp-growth algorithm to form an association model with the aim of helping companies increase their sales by creating underwear promotional packages. The data set that will be used to support this research is the sales transaction data set for the period April 2020 to December 2020. The results show that known rules have been obtained using the fp-growth algorithm, where the rules of this association can create strategies to increase clothing sales. In the form of five association rules that are ready to be used for making clothing promotional packages by meeting the support values and confidence values that have been set at the beginning, namely having a confidence value above 80% and a support value above 25%.

Loss prediction of three-level amplified spontaneous emission sources in radiation environment

A model of three-level amplified spontaneous emission (ASE) sources, considering radiation effect, is proposed to predict radiation induced loss of output power in radiation environment. Radiation absorption parameters of ASE sources model are obtained by the fitting of color centers generation and recovery process of and gain loss data at lower dose rate. Gain loss data at higher dose is applied for self-validating. This model takes both the influence of erbium ions absorption and photon bleaching effect into consideration, which makes the prediction of different dose and dose rate more accurate and flexible. The fitness value between ASE model and gain loss data is 99.98%, which also satisfies the extrapolation at the low dose rate. The method and model may serve as a valuable tool to predict ASE performance in harsh environment.

An Improved Cuckoo Search Clustering Method for Line Loss Data of Transformer District with DGs

For the low-voltage transformer district with distributed generations (DGs), the traditional theoretical calculation method of line loss is not applicable. This paper presents a novel clustering method for line loss data of transformer district with DGs, which combined an improved Cuckoo Search algorithm and K-Means clustering algorithm. Firstly, the influence factors of line loss are screened based on the maximum information coefficient, and the line loss index system is established. Secondly, an improved cuckoo search clustering algorithm is proposed to cluster the sample data set to reduce the dependence on the initial clustering center. Finally, the simulation results of 410 samples from a certain area with photovoltaic power supply show the accuracy and effectiveness of the proposed method. The simulation results show that the proposed method is accurate and effective.

Effects of Probability-Distributed Losses on Flood Estimates Using Event-Based Rainfall-Runoff Models

Probability distributions of initial losses are investigated using a large dataset of catchments throughout Australia. The variability in design flood estimates caused by probability-distributed initial losses and associated uncertainties are investigated. Based on historic data sets in Australia, the Gamma and Beta distributions are found to be suitable for describing initial loss data. It has also been found that the central tendency of probability-distributed initial loss is more important in design flood estimation than the form of the probability density function. Findings from this study have notable implications on the regionalization of initial loss data, which is required for the application of Monte Carlo methods for design flood estimation in ungauged catchments.