The Research of the Enterprise Financial Model Based on Information Entropy and Correlation Model Reorganization Theory

2015 ◽  
Author(s):  
Zhang Yinping
Keyword(s):  
Information Entropy ◽  
Correlation Model ◽  
Financial Model ◽  
Model Based

Spatial Correlation Interpretation of Effects of As+ Implantation on the Raman Spectra of GaAs

1983 ◽  
Vol 27 ◽  
Author(s):  
D.E. Aspnes ◽  
K.K. Tiong ◽  
P.M. Amirtharaj ◽  
F.H. Pollak
Keyword(s):  
Spatial Correlation ◽  
Raman Spectra ◽  
Selection Rule ◽  
Phonon Mode ◽  
Red Shift ◽  
Correlation Model ◽  
Model Based ◽  
Ion Implanted

ABSTRACTThe red shift and asymmetric broadening of the LO phonon mode of ion-implanted GaAs are both described quantitatively by a spatial correlation model based on a damage-induced relaxation of the momentum selection rule previously used by Richter, Wang, and Ley to describe similar effects in microcrystalline Si. The success of the model for a qualitatively different disorder microstructure suggests it may be possible to evaluate average sizes of crystallographically perfect regions in semiconductors from the phonon lineshapes of their Raman spectra.

Attribute Recognition Model Based on Entropy Weight and Its Application to Evaluation of Groundwater Quality

2010 ◽  
Vol 29-32 ◽  
pp. 2698-2702
Author(s):  
Xian Qi Zhang ◽  
Wen Hong Feng ◽  
Nan Nan Li
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Information Entropy ◽  
Simple Calculation ◽  
Weight Coefficient ◽  
Entropy Theory ◽  
Entropy Weight ◽  
Recognition Model ◽  
Model Based ◽  
Attribute Recognition

It is necessary to take into account synthetically attribute of every index because of independence and incompatibility resulted from single index evaluating outcomes. Through the information entropy theory and attribute recognition model being combined together, attribute recognition model based on entropy weight is constructed and applied to evaluating groundwater quality by a new method, weight coefficient by the law of entropy value is exercised so that it is more objective. The outcome from concrete application indicates that it is suitable to evaluate water quality with reasonable conclusion and simple calculation.

Machine-learned electron correlation model based on correlation energy density at complete basis set limit

2019 ◽  
Vol 151 (2) ◽  
pp. 024104 ◽  
Author(s):  
Takuro Nudejima ◽  
Yasuhiro Ikabata ◽  
Junji Seino ◽  
Takeshi Yoshikawa ◽  
Hiromi Nakai
Keyword(s):  
Energy Density ◽  
Electron Correlation ◽  
Correlation Energy ◽  
Basis Set ◽  
Correlation Model ◽  
Model Based ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Complete Basis Set Limit

scholarly journals A Forecasting Model Based on High-Order Fluctuation Trends and Information Entropy

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 669 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Information Entropy ◽  
Prediction Models ◽  
Stock Exchange ◽  
Two Dimensions ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Sets ◽  
Prediction Ability ◽  
Model Based ◽  
Logical Rules

Most existing high-order prediction models abstract logical rules that are based on historical discrete states without considering historical inconsistency and fluctuation trends. In fact, these two characteristics are important for describing historical fluctuations. This paper proposes a model based on logical rules abstracted from historical dynamic fluctuation trends and the corresponding inconsistencies. In the logical rule training stage, the dynamic trend states of up and down are mapped to the two dimensions of truth-membership and false-membership of neutrosophic sets, respectively. Meanwhile, information entropy is employed to quantify the inconsistency of a period of history, which is mapped to the indeterminercy-membership of the neutrosophic sets. In the forecasting stage, the similarities among the neutrosophic sets are employed to locate the most similar left side of the logical relationship. Therefore, the two characteristics of the fluctuation trends and inconsistency assist with the future forecasting. The proposed model extends existing high-order fuzzy logical relationships (FLRs) to neutrosophic logical relationships (NLRs). When compared with traditional discrete high-order FLRs, the proposed NLRs have higher generality and handle the problem caused by the lack of rules. The proposed method is then implemented to forecast Taiwan Stock Exchange Capitalization Weighted Stock Index and Heng Seng Index. The experimental conclusions indicate that the model has stable prediction ability for different data sets. Simultaneously, comparing the prediction error with other approaches also proves that the model has outstanding prediction accuracy and universality.

scholarly journals Research on measurement of supply chain finance credit risk based on Internet of Things

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987400 ◽  
Author(s):  
Waseem Ahmed Abbasi ◽  
Zongrun Wang ◽  
Yanju Zhou ◽  
Shahzad Hassan
Keyword(s):  
Supply Chain ◽  
Internet Of Things ◽  
Credit Risk ◽  
Measurement Model ◽  
The Internet ◽  
Financial Model ◽  
Model Based ◽  
Supply Chain Finance ◽  
Credit Risk Measurement ◽  
The Internet Of Things

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.

Trusted Software Construction Model Based on Trust Shell

2011 ◽  
Vol 186 ◽  
pp. 251-255 ◽  
Author(s):  
Jun Feng Tian ◽  
Ye Zhu
Keyword(s):  
Information Entropy ◽  
Simulation Experiment ◽  
Trusted Computing ◽  
Check Point ◽  
Practical Application ◽  
Model Based ◽  
Methods And Techniques ◽  
Software Construction ◽  
Construction Model ◽  
Development Methods

Due to not considering the guaranty of trustiness, traditional software development methods and techniques lack effective measures for ensuring trustiness. Combining agent technique with trusted computing provided by TPM, a trusted software construction model based on Trust Shell (TSCMTS) is demonstrated in this paper, where Trust Shell is responsible for ensuring the trustiness of software logically. In particular, for the purpose of improving the accuracy of trustiness constraints, a strategy of determining multiple attributes’ weights by information entropy for check point is proposed. Both simulation experiment results and practical application indicate that the TSCMTS is of effective trustiness and reasonable performance overhead.

Sign in / Sign up

Export Citation Format

Share Document