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.

Realization of an Intelligent E-Commerce Model Based on Mobile Agent

2014 ◽  
Vol 543-547 ◽  
pp. 4198-4201
Author(s):  
Xiao Guang Li ◽  
Zhan Jun Gao
Keyword(s):  
Mobile Agent ◽  
Mobile Agents ◽  
Simulation Experiment ◽  
The Internet ◽  
Variable Time ◽  
Model Based

Mobile agent is one of the most prominent technologies believed to be playing an important role in future e-commerce. After presented an intelligent e-commerce model based on OBI ( open buying on the internet) , we developed a modified approach for the security of mobile agents and e-commerce, and designed an intelligent shopping algorithm based on variable time negotiation function. The presented model has been evaluated by simulation experiment. It has been found that the presented model is efficient.

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.

Fuzzy Matter-Element Evaluation Method for Analysis of Debris Flow Risk Assessment

2012 ◽  
Vol 446-449 ◽  
pp. 3058-3061 ◽  
Author(s):  
Chun Tan ◽  
Jian Ping Chen ◽  
Yu Zhen Pan ◽  
Cen Cen Niu ◽  
Li Ming Xu
Keyword(s):  
Risk Assessment ◽  
Debris Flow ◽  
Information Entropy ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Practical Application ◽  
Element Analysis ◽  
Matter Element Analysis ◽  
Concept Of Information

Based on the principle of fuzzy matter-element analysis, the concept of information entropy is introduced to establish a fuzzy matter-element evaluation method. This method is utilized to comprehensively evaluate the degree of debris flow. The classifications of debris flow are regarded as the objects of matter-element and their indexes for evaluation as well as the corresponding fuzzy values are used to construct the composite fuzzy matter-elements. By calculating the relevancy the comprehensive evaluation of debris flow can be carried out. This model is applied to analyze the degree of debris flow in the practical application. The application shows that the model is effective and practical.

scholarly journals Correct by construction model based design for systems and software

2018 ◽  
Author(s):  
Εμμανουέλα Στάχτιαρη
Keyword(s):  
Model Based ◽  
Construction Model

Η παρούσα διατριβή εισάγει τεχνικές για την αυστηρή και από κατασκευής ορθή σχεδίαση συστημάτων. Ειδικότερα, εστιάσαμε στο πώς μπορούμε να παράγουμε και να επικυρώνουμε ένα λειτουργικό μοντέλο της εφαρμογής που προκύπτει από ένα σύνολο απαιτήσεων ή από τον κώδικα της εφαρμογής. Αρχικά, ασχοληθήκαμε με την πρώιμη επικύρωση των απαιτήσεων και της σχεδίασης του συστήματος, ώστε να εξαλειφθεί η ανάγκη επαλήθευσης εκ των υστέρων και να περιοριστούν οι έλεγχοι επικύρωσης κατά τα τελευταία στάδια ανάπτυξης. Δεύτερον, εστιάσαμε στην αυτόματη δημιουργία λειτουργικών μοντέλων εφαρμογών από προγράμματα με εμφωλευμένη σύνταξη, διατηρώντας τη σημασιολογία των προγραμμάτων. Τέλος, προτείναμε μία ροή σχεδίασης που αποσκοπεί στη διατήρηση της συνέπειας μεταξύ του λειτουργικού μοντέλου και του κώδικα εφαρμογής μέσω της χρήσης μιας νέας γλώσσας ειδικού σκοπού, κατάλληλης για τη σχεδίαση συστημάτων περιορισμένων πόρων του διαδικτύου των αντικειμένων.

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.

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