Application of digital engineering methods in the agro-industrial complex on the example of crop productivity forecasting

The research is dedicated to the development of a method for quantifying the productivity of an agricultural crop with a long lifespan (using the example of an apple orchard). To achieve this goal, the possibility of predicting the yield of an apple orchard was evaluated using existing techniques and methods of data analysis; agrotechnical significant time points in the life cycle of an apple orchard were identified; a prognostic model was formed that simultaneously satisfies biological and agrotechnical constraints and provides the maximum tier of reliability of the yielding forecast result available for the crop under consideration. It is shown that the cumulative yield of an apple orchard lends itself to forecasting much better than the dynamics of the annual yield. As a consequence, in strategic planning in the agricultural and industrial complex, it makes sense to focus on integral performance indicators that level out deviations caused to varying degrees by random causes.

Forecasting algorithm of tourism service trade based on PSO-optimized hybrid RVM model

As a comprehensive form of trade, tourism service trade has had a profound impact on the economies of various countries. This research mainly discusses the tourism service trade forecasting algorithm based on the PSO-optimized hybrid RVM model. This study extracts 8 indicators including gross national product, total fixed asset investment, total import and export, China's import and export tariff rate, the exchange rate of renminbi to the US dollar, and the global economic growth rate. The same as the impact indicators of tourism service trade, but there is a certain degree of redundancy and correlation in these indicators. In order to measure the correlation between the evaluation indicators, the autocorrelation evaluation function in MATLAB is used, and the principal component analysis method is used to extract the principal components that can represent the indicators in a larger percentage. In order to improve the prediction accuracy of the RVM model, based on the adaptive construction model structure and initial model weights, the PSO algorithm is used to optimize the RVM model weights. The optimization process takes the minimum error of the RVM model as the algorithm search target, and each represents the RVM model. The algorithm finds the value and threshold of the optimal RVM model through the particle swarm tracking search algorithm and then uses the original RVM model and the optimized RVM prediction respectively total amount of tourism service trade in City A, and compares the prediction errors of the single RVM method and the PSO-optimized RVM method, and analyzes the degree of model prediction error reduction after the PSO model optimizes the RVM model. According to the forecast result, the relative average error of 2020 is 5.7%, and the forecast result is relatively accurate. This research is helpful to provide scientific reference for my country's tourism service trade.

Forecasting the Inequality of Income Distribution in Consequence of the Covid-19 Pandemic

This study aims to forecasting the Covid-19 Pandemic's effect on income inequality distribution in Kulon-Progo Regency during of 2020 to 2028. The study analysis tools utilized forecast are linear and non-linear trend. The historical data use during of 2010 to 2019, data source obtained from Central Bureau of Statistics Yogyakarta in statistical series book of 2020. The findings of forecast result show that the Covid-19 pandemic directly impact on the increased income inequality distribution. The implication is to carry out the process of economic recovery due to the Covid-19 pandemic case by identifying community groups who are vulnerable to decreased income through strengthening social safety nets. In addition, government policies can also optimize the utilization and transportation services to increase farmer exchange rates, because most people work in the agricultural sector.

Residual Convolution LSTM Network for Machines Remaining Useful Life Prediction and Uncertainty Quantification

Recently, deep learning is widely used in the field of remaining useful life (RUL) prediction. Among various deep learning technologies, recurrent neural network (RNN) and its variant, e.g., long short-term memory (LSTM) network, are gaining more attention because of their capability of capturing temporal dependence. Although the existing RNN-based approaches have demonstrated their RUL prediction effectiveness, they still suffer from the following two limitations: 1) it is difficult for RNN to extract directly degradation features from original monitoring data, and 2) most of the RNN-based prognostics methods are unable to quantify the uncertainty of prediction results. To address the above limitations, this paper proposes a new method named Residual convolution LSTM (RC-LSTM) network. In RC-LSTM, a new ResNet-based convolution LSTM (Res-ConvLSTM) layer is stacked with convolution LSTM (ConvLSTM) layer to extract degradation representations from monitoring data. Then, predicated on the RUL following a normal distribution, an appropriate output layer is constructed to quantify the uncertainty of the forecast result. Finally, the effectiveness and superiority of RC-LSTM is verified using monitoring data from accelerated degradation tests of rolling element bearings.

Pemanfaatan Metode Association Rules dan Holt-Winter Multiplicative untuk Meningkatkan Peluang Penjualan Obat Pertanian

The competition level between companies on executing product marketing is rapidly increasing, so the companies have to understand the importance of correlation between external environments of company with consumer’s needs. One of the efforts that can be done is by utilizing data warehouse and the application of infrastructure in information and technology field. This research combined Association Rules method to extracting pattern and finding every possibility that potential to increase sales and Holt-Winter Multiplicative method to estimate the alteration of trend on the seasonal data. After passed through data processing process by using RapidMiner tools, information that consists of correlation pattern between rule that describe the comparison of product and the sales working area and season that affects the product sale. The pattern used by company to know which product is often purchased by customer. Besides that, this research produces changing trend data of PT ABC’s product that generated by result of previous data comparison with forecast data. Based on value of error rate Mean Absolute Percentage Error (MAPE) in estimating forecast result on the PT ABC’s sales transaction data during 3 years, it shows good level of accuracy. Result of data test, by considering rule that formed and forecast result so the company can control and manage product in order to avoid incorrect sales. This thing will effect on repression of operational cost and PT ABC can identify available opportunities to increase sale of agricultural medicine.

Analisis Pengendalian Persediaan Bahan Baku Kerupuk Mentah Potato Dan Kentang Keriting Menggunakan Metode Economic Order Quantity (EOQ)

<em>The increase in the food industrial sector over the year making food producers experience tighter competition. In this era of intense competition in the food industry, raw material inventory control in PT. Surya Indah Food Multirasa is still facing a problem of lacking raw material in potatoes and curly potatoes every year, which affects in sale loss. This study is conducted to find out the precise amount of order in every raw material purchase, so there is no lack of raw material, at a lower cost. This study uses the Economic Order Quantity (EOQ) method for analyzing raw material control for potatoes and curly potatoes. Comparing the uses of company policy and EOQ. EOQ analysis calculation followed by conducting safety stock (SS) analysis, maximum inventory (MI) analysis, Total Inventory Cost (TIC) analysis, and reorder point (ROP) analysis, therefore, optimal stock for the company can be discovered. The result of EOQ analysis showed that the EOQ method is more efficient than company policy with TIC average difference for potato’s raw material that is Rp. 856.124 and curly potatoes raw material that is Rp. 1.065.989. The average EOQ value of potato’s raw material is 344 kg while curly potatoes are 234 kg. Next is the average SS value for potato raw material is 75 kg while curly potatoes are 35 kg, and the average ROP value for potato raw is 123 kg while curly potatoes are 58 kg. This research also performs forecasting using Winter’s method so that data demand for the year of 2019 can be discovered. The forecast result with the EOQ method for the year 2019 is 371 kg for potato's raw material and 258 kg for curly potatoes.</em>

ANALISA PERENCANAAN KAPASITAS PRODUKSI PADA PT. MUNCUL ABADI DENGAN METODE ROUGH CUT CAPACITY PLANNING

Production capacity planning this company have obstacle in meeting its production target, then company oftentimes unable to fulfill consumer demands. It can be inflict the company loose. The problems is how planning production capacity based on Rough Cut Capacity Planning (RCCP) Method in order to make consumers demand be able to supplied? “ The researcher attempt to resolving problem in PT Muncul Abadi by aiming calculate product capacity plan based on Rough Cut Capacity Planning Method and determine required product. To be useful as consideration for the company in planning production. Forecast done within the coming one year term. Capacity planning base on Rough Cut Capacity Planning (RCCP) Method. From forecast result toward previous demand quantity period within 12 periods we could be make production index schedule and order Bill of Resources with standard time that is 0,000316 hours/kg. Able to know machine capacity need in 1 workday = 8 hours day, with 3 shifts per day, 1 week = 6 workdays, 1 month = 25 effective workdays then : available time per month = 25 effective workdays x 8 hours day x 3 shift per day = 600 hours/month. Available capacity for Washing machine = 1.394 hours/month. Available capacity for Crushing machine = 1.394 hours/month. Available capacity for Pelletizing machine = 1.859 hours/month.

Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators

Sulfur dioxide is an important source of atmospheric pollution. Many countries are developing policies to reduce sulfur dioxide emissions. In this paper, a novel prediction model is proposed, which could be used to forecast sulfur dioxide emissions. To improve the modeling procedure, fractional order accumulating generation operator and fractional order reducing generation operator are introduced. Based on fractional order operators, a discrete grey model with fractional operators is developed, which also makes use of genetic algorithms to optimize the modeling parameter r. The improved performance of the model is demonstrated via comparison studies with other grey models. The model is then used to predict China’s sulfur dioxide emissions. The forecast result shows that the amount of sulfur dioxide emissions is steadily decreasing and the policies of sulfur dioxide reduction in China are effective. According to the current trend, by 2020, the value of China’s sulfur dioxide emissions will be only 86.843% of emissions in 2015. Fractional order generation operators can be used to develop other fractional order system models.

Export injury early warning of the new energy industries in China

Purpose In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models. Design/methodology/approach In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018. Findings Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade. Practical implications Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure. Originality/value This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.

The Implementation of Grey Forecasting Model for Forecast Result�s Food Crop Agricultural

The increasing of the needs of food crops raised several issues related to land use. The problems of land used caused by the lack of information related to productivity and eligibility used of land. The goal of this research is to implementation a model of Grey forecasting GM(1,1) to forecast agricultural production, especially in food crops. GM(1,1) is used to built a model with limited data samples and generate good forecasts for short libertine forecasts. This research uses data from the production of food crops for the 2004-2013 it can be calculated by using the model of GM (1,1). The results showed the model GM (1,1) can produce highly accurate forecasts, from the experimental results for pattern trends generate value ARPE 5.74% or accuracy of forecasts reached 94.26% in crop production.