222 research outputs found
Proof-of-Work Difficulty Readjustment with Genetic Algorithm
No full textBlockchain is a decentralized, distributed and public digital ledger technology. It can be visualized as a gradually increasing list of “blocks” which contains data that are linked together using cryptographic hash. Each transaction is verified by several participating nodes to compute a complex mathematical problem. The complexity of this computation, also known as Proof-of-Work (PoW), is governed by the difficulty set on a periodic basis. If the hash rate of the blockchain’s PoW grows or declines exponentially, the blockchain will be unable to maintain the block creation interval. The utilization of genetic algorithm (GA) in addition with the existing difficulty adjustment algorithm is proposed as a response to this by optimizing the blockchain parameters. A simulation of 3 scenarios as well as the default, were performed and the results were recorded. Based on the results, we are able to observe that the blockchain is able to reach the expected block time 74.4% faster than the blockchain without GA. Moreover, the standard deviations of the average block time and difficulty decreased by 99.4% and 99.5% respectively when block and difficulty intervals were considered for optimization, when compared to the default blockchain without GA
Analysis of power balance in a helicon plasma source
No full textThis thesis analyses the Helicon Injected Inertial Plasma Electrostatic Rocket (HIIPER), a space propulsion concept consisting of a helicon source for plasma generation and an ion extraction method using a nested pair of inertial electrostatic confinement (IEC) grids that are asymmetrically designed. In this study, the HIIPER setup was modelled based on the previous experimental data of the plasma characteristics obtained to account for various power loses and understand the major contributors to the low efficiency and final thrust performance. The loses to the quartz tube wall was substantially higher than the loses at the metal bellow region and the loses due to ionization and excitation. Future improvements in Langmuir probe design to characterize the plasma and the feasible changes to the experimental setup are provided in this study.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-05-01The student, Hariharan Manickam Vaithiyanathan, accepted the attached license on 2020-04-30 at 11:52.The student, Hariharan Manickam Vaithiyanathan, submitted this Thesis for approval on 2020-04-30 at 12:09.This Thesis was approved for publication on 2020-05-12 at 11:33.DSpace SAF Submission Ingestion Package generated from Vireo submission #15126 on 2020-08-25 at 17:28:30Made available in DSpace on 2020-08-26T23:57:20Z (GMT). No. of bitstreams: 2
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Previous issue date: 2020-05-12Embargo set by: Seth Robbins for item 115750
Lift date: 2022-08-26T23:57:28Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 115750
Lift date: 2022-08-26T23:58:55Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
An Ontology-Driven Methodology To Derive Cases From Structured And Unstructured Sources
No full textThe problem-solving capability of a Case-Based Reasoning (CBR) system largely depends on the richness of its knowledge stored in the form of cases, i.e. the CaseBase (CB). Populating and subsequently maintaining a critical mass of cases in a CB is a tedious manual activity demanding vast human and operational resources. The need for human involvement in populating a CB can be drastically reduced as case-like knowledge already exists in the form of databases and documents and harnessed and transformed into cases that can be operationalized. Nevertheless, the transformation process poses many hurdles due to the disparate structure and the heterogeneous coding standards used. The featured work aims to address knowledge creation from heterogeneous sources and structures. To meet this end, this thesis presents a Multi-Source Case Acquisition and Transformation Info-Structure (MUSCATI). MUSCATI has been implemented as a multi-layer architecture using state-of-the-practice tools and can be perceived as a functional extension to traditional CBR-systems. In principle, MUSCATI can be applied in any domain but in this thesis healthcare was chosen. Thus, Electronic Medical Records (EMRs) were used as the source to generate the knowledge. The results from the experiments showed that the volume and diversity of cases improves the reasoning outcome of the CBR engine. The experiments showed that knowledge found in medical records (regardless of structure) can be leveraged and standardized to enhance the (medical) knowledge of traditional medical CBR systems. Subsequently, the Google search engine proved to be very critical in “fixing” and enriching the domain ontology on-the-fly
Improved Mechanism to Prevent Denial of Service Attack in IPv6 Duplicate Address Detection Process
No full textControl update frequency of queue changes velocity for congestion management in high-speed networks
No full textChemInform Abstract: One‐Pot Synthesis of Benzothiazole‐Tethered Chromanones/Coumarins via Claisen Rearrangement Using the Solid State Melt Reaction.
No full textAdditions and corrections: Nano a-NiMoO4 as new electrode for electrochemical supercapacitors
No full textTwo of the author names are incorrectly written, showing the surname as the first name, and the first name as the surname. The correct names are Danielle Meyrick and Manickam Minaksh
An enhanced adaptive grey verhulst prediction model for network security situation
No full textSituation prediction is an increasingly important focus in network security. The information of incoming security situation in the network is important and helps the network administrator to make good decisions before taking some defense remedies towards the attack exploitation. Although Grey Verhulst prediction model has demonstrated satisfactory results in other fields but some further investigations are still required to improve its performance in predicting incoming network security situation. In order to attain higher predictive accuracy of the existing Grey Verhulst prediction models, this paper tends to seek an enhancement of the adaptive Grey Verhulst security situation prediction model by forecasting the incoming residual based on the historical prediction residuals. The proposed model applied Kalman Filtering algorithm to predict the residual in the next time-frame and closer the deviation between the predicted and actual network security situation. Benchmark datasets such as DARPA 1999 and 2000 have been used to verify the accuracy of the proposed model. The results shown that the enhanced adaptive Grey Verhulst prediction model has better prediction capability in predicting incoming network security situation and also achieved a significant improvement Verhulst prediction models
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