Fuzzy integrated Bayesian Dempster–Shafer theory to defend cross-layer heterogeneity attacks in communication network of Smart Grid

2019 ◽  
Vol 479 ◽  
pp. 542-566 ◽  
Author(s):  
Durgadevi Velusamy ◽  
Ganesh Kumar Pugalendhi
Keyword(s):  
Communication Network ◽  
Smart Grid ◽  
Cross Layer ◽  
Dempster Shafer Theory ◽  
Shafer Theory

MULTY UTILITY SERVICE INFRASTRUCTURE (MUSI) BERBASIS OPLC UNTUK IMPLEMENTASI SMART GRID COMMUNICATION NETWORK

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Ceceng Muhaemin
Keyword(s):  
Communication Network ◽  
Smart Grid ◽  
Smart Grid Communication

Lahirnya konsep Smart Grid tidak lepas dari issue global terkait dampak lingkungan hidup akibat penggunaan energy berbahan fosil secara massif yang semakin hari cadangan energy yang tersimpan akan habis dalam beberapa tahun kedepan dan diperlukan sumber energy baru yang terbarukan (non fosil).  Implementasi Smart Grid sudah banyak di terapkan dibeberapa negara, termasuk di Indonesia, konsep Smart Grid dan prototipe sudah diimplementasikan dibeberapa kota walau populasinya belum banyak. Pilar utama selain listrik, dalam konsep Smart Grid ini adalah komunikasi dan IT, dimana diperlukan komunikasi dua arah antar mesin dan saling terintegrasi antar grid. Pada kanvas ini diusulkan konsep MUSI (Multy Utility Service Infrastructure) sebagai media komunikasi dalam implementasi Smart Grid dengan menggunakan jenis kabel OPLC (Optical Low Composite Cable), dimana dua infrastruktur yang berbeda menjadi satu konsep. Pada pembahasan ini metode yang digunakan adalah studi literature, pengamatan dan data, ondesk survey, analisa keekonomian (techno economy) dan pengambilan kesimpulan. Hasil dari analisa finansial yang dilakukan bahwa dengan menggunakan model MUSI yang diajukan, NPV dengan MARR 20% didapatkan nilai positif, dan IRR sekitar 34%, sementara jika dilakukan dengan metode konvensional, NPV dengan MARR 20% didapatkan nilai negatif, dan IRR dibawah 0% (negatif).

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

scholarly journals Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German
Keyword(s):  
Sensor Fusion ◽  
A Priori ◽  
Evidential Reasoning ◽  
Unmanned Aerial Systems ◽  
Sufficient Information ◽  
User Input ◽  
Transition Potential ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Aerial Systems

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.

An application of Dempster-Shafer theory to the assessment of biogas technology

Energy ◽  
1992 ◽  
Vol 17 (3) ◽  
pp. 205-214
Author(s):  
Aurora A. Kawahara ◽  
Peter M. Williams
Keyword(s):  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Biogas Technology
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