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In this paper, by using grey system theory, grey relation analysis and trend prediction was carried out for the influencing factors of agricultural mechanization development levels in major granary provinces in China. At present, the total power of agricultural mechanization, total income from agricultural mechanization, fuel consumption for agricultural production have great influence on the agricultural...
The paper is introducing an adoption of Industry 4.0 Concept on Smart City theoretic model. Cities are upgraded with latest computing and ICT technologies throughout all systems and infrastructure. New data are collected in big amounts and ability to use them is changing how city subsystems can communicate together, to make cities work better and serve better to their users. Smart Cities are convoluted...
Linear regression is a standard statistical method widely used for prediction. It focuses on modeling the mean the target variable without accounting for all the distributional properties of this variable. In contrast, the quantile regression model facilitates the analysis of the full distributional properties, it allows to model different quantities of the target variable. This paper proposes a quantile...
In this paper we present a HAZOP Assistant based on D-higraphs and dedicated to a functional modeling technique that gathers functional and structural information of the process under study. The Assistant and the methodology are presented and applied to a High Density Polyethylene reactor part of the CP2K plant situated in the petrochemical platform of Skikda.
Apparel manufacturing is a highly labor-intensive industry where the most operations require highly skilled human worker involvement. Therefore the production performance of apparel manufacturing is mainly worker-dependent. Workers improve their performance in a task as repetitions take place. This phenomenon is called as the “Learning Curve”, studied by researchers and is largely prevalent in the...
In order to overcome the shortcomings of the conventional model GM (1,N), a new optimized model which is based on the development trends of multiple driving variables, termed TMGM (1,N), is proposed in this study. The new model has improved the theory from the following aspects. First, a new forecast model of the development trend of the driving variables is established which can make better use of...
Micro grid represents an emergent paradigm to address the challenges of recent smart electrical grid visions, where several small-scale and distributed electrical units cooperate to achieve higher levels of energy self-sustainability, by reducing the main grid dependence. Nevertheless, the realization of this paradigm requires advanced intelligent approaches that are able to effectively manage the...
This paper concentrates on the problem of safety assessment for down-hole casings in well completion, and we constrain the environment in high temperature and high pressure. Firstly, in order to analyze casing wear depth, we establish a suitable wear prediction model, in which friction and sliding distance coefficient between drill string and down-hole casing are utilized. Secondly, a novel safety...
Aimed to improve accuracy rate and time advance of the Anode Effect (AE) prediction in aluminum electrolysis cell, a new prediction method by means of the Generalized Regression Neural Network (GRNN) is proposed. The structure and advantages of the GRNN are introduced, then modeling the anode effect system of aluminum electrolysis cell by means of system identification based on the GRNN. The structure...
BIPV market is growing fast and contributing to zero-energy buildings. Installing PV systems on walls is unusual, however it has a huge potential to stimulate zero-energy buildings. The production of energy from photovoltaic panels installed vertically is difficult to predict due to some reasons like the interference of shadows caused by nearby buildings or adjacent vegetation, and the lower yield...
This paper presented three types of models for forecasting the supply and demand of Thai ethanol, so called MR, ANN, and MR-ANN models. MR models were formulated using stepwise multiple regression analysis, which were statistically significant. However, MR models provided low performance in forecasting. ANN models were constructed using artificial neural networks, which provided satisfactory results...
When inspection economies are implemented in multi-product, multi-stage, parallel processing manufacturing systems, there exists a significant risk of losing control of the monitoring efficacy of the sampling strategy adopted. For a product-based sampling decision limited to a particular station in a production segment, the randomness of the departure process and the merging of different product flows...
Reservoir simulation software is an important tool in oil and gas industries to predict the multiphase flow of reservoirs. The output from reservoir simulation consists of production history, reservoir pressure, grid block saturation, porosity and permeability change etc. Due to the intrinsic set of uncertainty in reservoir simulation prediction, considerable number of simulation runs to be performed...
With the continuous development of flexible manufacturing technology, flexible management methods are needed in all kinds of production process. This paper introduces flexible process management method in capturing, tracking, testing, and evaluating the equipment's condition and its remaining lives. Based on the ever enriched flexible connotation, it is extended into the matching process management...
California is the leading agricultural state in the United States. Agricultural production is being threatened by many factors: loss of agricultural land, water shortage, among others. In this paper, we employed an ordinary least square regression (OLS) model and a geographically weighted regression (GWR) model to examine the relationship between farming viability in California and three contributing...
This paper investigates the recent evolution of the oil price, with the objective to analyze the main drivers that during last fifteen years have led the unstable path and the volatility persistence in the international oil market. We assume that the oil price is composed by two components, deterministic and speculative. The first one can be defined as the certain one, and it is referred to the fundamental...
We model and evaluate the performance of a distributed key-value storage system that is part of the Spotify backend. Spotify is an on-demand music streaming service, offering low-latency access to a library of over 16 million tracks and serving over 10 million users currently. We first present a simplified model of the Spotify storage architecture, in order to make its analysis feasible. We then introduce...
This paper uses the support vector machine (SVM) algorithm to study the prediction of corn production in Heilongjiang province, forms the sample set with the 1991-2008 data in Heilongjiang province, and set up the SVM model between factors and corn production. Use SVM on the input and output data for training and learning, approximate the implied function relationship by historical data, complete...
In recent years the extreme weather disasters are occurring more and more frequently, which caused a great impact on the economy. After comparing with 5 kinds of methods in the evaluation losses of natural disasters field, that is comprehensive evaluation model, econometric model, IO model, SAM model and CGE model, this paper contributes a comprehensive analysis strategy in condition of different...
As one of the important economic provinces in China, Shandong province plays an important role in energy production and energy consumption. Based on the revised time-series data of the energy production and supply in 1985-2009 in Shandong province, this study has established a grey forecasting model GM (1, 1) and made the accuracy checking for it. Smoothing test and exponential test are employed here...
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