Appendix D: The Input and Output Data for the Hypothetical Mine

2019 ◽  
pp. 483-491
Keyword(s):  
Output Data ◽  
Input And Output

scholarly journals A Profit Distribution Model of Reverse Logistics Based on Fuzzy DEA Efficiency—Modified Shapley Value

2021 ◽  
Vol 13 (13) ◽  
pp. 7354
Author(s):  
Jiekun Song ◽  
Xiaoping Ma ◽  
Rui Chen
Keyword(s):  
Shapley Value ◽  
Reverse Logistics ◽  
Service Providers ◽  
Third Party ◽  
Output Data ◽  
Profit Distribution ◽  
Logistics Service ◽  
Input And Output ◽  
Value Model ◽  
Fuzzy Dea

Reverse logistics is an important way to realize sustainable production and consumption. With the emergence of professional third-party reverse logistics service providers, the outsourcing model has become the main mode of reverse logistics. Whether the distribution of cooperative profit among multiple participants is fair or not determines the quality of the implementation of the outsourcing mode. The traditional Shapley value model is often used to distribute cooperative profit. Since its distribution basis is the marginal profit contribution of each member enterprise to different alliances, it is necessary to estimate the profit of each alliance. However, it is difficult to ensure the accuracy of this estimation, which makes the distribution lack of objectivity. Once the actual profit share deviates from the expectation of member enterprise, the sustainability of the reverse logistics alliance will be affected. This study considers the marginal efficiency contribution of each member enterprise to the alliance and applies it to replace the marginal profit contribution. As the input and output data of reverse logistics cannot be accurately separated from those of the whole enterprise, they are often uncertain. In this paper, we assume that each member enterprise’s input and output data are fuzzy numbers and construct an efficiency measurement model based on fuzzy DEA. Then, we define the characteristic function of alliance and propose a modified Shapley value model to fairly distribute cooperative profit. Finally, an example comprising of two manufacturing enterprises, one sales enterprise, and one third-party reverse logistics service provider is put forward to verify the model’s feasibility and effectiveness. This paper provides a reference for the profit distribution of the reverse logistics.

scholarly journals Data derived NARMAX Dst model

2011 ◽  
Vol 29 (6) ◽  
pp. 965-971 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings ◽  
A. S. Sharma ◽  
O. A. Amariutei
Keyword(s):  
Mathematical Model ◽  
Solar Wind ◽  
System Identification ◽  
Nonlinear Systems ◽  
Wide Class ◽  
Geomagnetic Storms ◽  
Output Data ◽  
Dst Index ◽  
Geomagnetic Indices ◽  
Input And Output

Abstract. The NARMAX OLS-ERR methodology is applied to identify a mathematical model for the dynamics of the Dst index. The NARMAX OLS-ERR algorithm, which is widely used in the field of system identification, is able to identify a mathematical model for a wide class of nonlinear systems using input and output data. Solar wind-magnetosphere coupling functions, derived from analytical or data based methods, are employed as the inputs to such models and the outputs are geomagnetic indices. The newly deduced coupling function, p1/2V4/3BTsin6(θ/2), has been implemented as an input to model the Dst dynamics. It was shown that the identified model has a very good forecasting ability, especially with the geomagnetic storms.

An LQ Controller With a Prescribed Pole Region—A Data-Based Design Approach

1997 ◽  
Vol 119 (2) ◽  
pp. 271-277 ◽  
Author(s):  
Jenq-Tzong H. Chan
Keyword(s):  
Closed Loop ◽  
Explicit Knowledge ◽  
Controller Design ◽  
Design Approach ◽  
Output Data ◽  
Plant Model ◽  
Modified Method ◽  
Input And Output ◽  
Z Domain

In this paper, we present a modified method of data-based LQ controller design which is distinct in two major aspects: (1) one may prescribe the z-domain region within which the closed-loop poles of the LQ design are to lie, and (2) controller design is completed using only plant input and output data, and does not require explicit knowledge of a parameterized plant model.

Concept of a System Architecture for a Simulation Data Management In the Digital Twin

2021 ◽  
Author(s):  
Benjamin Röhm ◽  
Reiner Anderl
Keyword(s):  
Product Development ◽  
Data Management ◽  
Input Data ◽  
Integrated Design ◽  
Simulation Data ◽  
Output Data ◽  
Digital Twin ◽  
Input And Output ◽  
Simulation Input ◽  
Digital Shadow

Abstract The Department of Computer Integrated Design (DiK) at the TU Darmstadt deals with the Digital Twin topic from the perspective of virtual product development. A concept for the architecture of a Digital Twin was developed, which allows the administration of simulation input and output data. The concept was built under consideration of classical CAE process chains in product development. The central part of the concept is the management of simulation input and output data in a simulation data management system in the Digital Twin (SDM-DT). The SDM-DT takes over the connection between Digital Shadow and Digital Master for simulation data and simulation models. The concept is prototypically implemented. For this purpose, real product condition data were collected via a sensor network and transmitted to the Digital Shadow. The condition data were prepared and sent as a simulation input deck to the SDM-DT in the Digital Twin based on the product development results. Before the simulation data and models are simulated, there is a comparison between simulation input data with historical input data from product development. The developed and implemented concept goes beyond existing approaches and deals with a central simulation data management in Digital Twins.

Dynamic System Identification Using a Step Input

2005 ◽  
Vol 24 (2) ◽  
pp. 125-134
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
Transfer Function ◽  
Step Response ◽  
Output Data ◽  
Mass System ◽  
Input And Output ◽  
Rational Transfer ◽  
Line Identification ◽  
The Moment

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady state output response such as a step response that causes noise or vibration from a mechanical system at the moment when it is applied but they are attenuated asymptotically. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0 ∼ N-tuple integral values of output error and irrelevant to the unmodelled dynamics. From the intermediate parameters, the coefficients of a rational transfer function are calculated. In consequence, the method can be executed for any plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a 2-mass system.

REPRESENTATION FOR DISPERSION FORMULA OF OPTICAL FIBER USING HYBRID TECHNIQUE

2008 ◽  
Vol 19 (02) ◽  
pp. 205-213 ◽  
Author(s):  
AMR RADI
Keyword(s):  
Neural Network ◽  
Refractive Index ◽  
Optical Fiber ◽  
Hybrid Technique ◽  
Training Set ◽  
Fiber System ◽  
Output Data ◽  
Input And Output ◽  
Dispersion Formula ◽  
The Neural Network

Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) which represents dispersion formula of optical fiber. An efficient NN has been designed by GA to simulate the dynamics of the optical fiber system which is nonlinear. Without any knowledge about the system, we have used the input and output data to build a prediction model by NN. The neural network has been trained to produce a function that describes nonlinear system which studies the dependence of the refractive index of the fiber core on the wavelength and temperature. The trained NN model shows a good performance in matching the trained distributions. The NN is then used to predict refractive index that is not presented in the training set. The predicted refractive index had been matched to the experimental data effectively.

Modeling and Analysis of Genetic Algorithms Based on the Viewpoint of Mixture Systems

2003 ◽  
Vol 7 (3) ◽  
pp. 268-275
Author(s):  
Jun-ichi Imai ◽  
◽  
Hiroyuki Shioya ◽  
Masahito Kurihara ◽  
Keyword(s):  
Genetic Algorithms ◽  
Mixture Models ◽  
Information Source ◽  
Point Of View ◽  
Input Output ◽  
Output Data ◽  
Modeling And Analysis ◽  
Input And Output ◽  
Partial Systems ◽  
Theoretical Analyses

Some mathematical models have been proposed for theoretical analyses of genetic algorithms (GAs). However, these works have limited their objects to a few kinds of GAs in order to formulate them accurately. In this paper, we regard a GA as an information source that generates input-output data. That is, we regard a population and its next population generated by the GA as input and output respectively. Then we model the GA by learning from these data. Since this method uses only the input-output relations of data and ignores interior structures, we can describe a variety of GAs in a common form, and analyze them from a new point of view. We use some mixture models for a representation of these input-output relations in this paper. By using a mixture model for modeling a GA, we can represent the GA system as a combination of some partial systems. In this paper, we treat two types of mixture models, and investigate how these models are effective for analyzing GAs through some numerical experiments.

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