Performance analysis of short-range NLOS UV communication system using Monte Carlo simulation based on measured channel parameters

Controlling texture and microstructure evolution during annealing processes is very important for optimizing properties of steels. Theories used to explain annealing processes are complicated and always case dependent. An recently developed Monte Carlo simulation based model offers an effective tool for studying annealing process and can be used to verify the arbitrarily defined theories that govern such processes. The computer model takes Orientation Image Microscope (OIM) measurements as an input. The abundant information contained in OIM measurement allows the computer model to incorporate many structural characteristics of polycrystalline materials such as, texture, grain boundary character, grain shape and size, phase composition, chemical composition, stored elastic energy, and the residual stress. The outputs include various texture functions, grain boundary and grain size statistics that can be verified by experimental results. Graphical representation allows us to perform virtual experiments to monitor each step of the structural transformation. An example of applying this simulation to Si steel is given.

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A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Electronics ◽

10.3390/electronics10222881 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2881

Author(s):

Muath Alrammal ◽

Munir Naveed ◽

Georgios Tsaramirsis

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Information Systems ◽

Malware Detection ◽

New Model ◽

Malicious Activity ◽

Model Based ◽

Simulation Based ◽

Computer Based ◽

Reinforcement Model

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

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A Monte Carlo simulation based chaotic differential evolution algorithm for scheduling a stochastic parallel processor system

Expert Systems with Applications ◽

10.1016/j.eswa.2015.05.015 ◽

2015 ◽

Vol 42 (20) ◽

pp. 7132-7147 ◽

Cited By ~ 19

Author(s):

Hadi Mokhtari ◽

Ali Salmasnia

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Differential Evolution ◽

Differential Evolution Algorithm ◽

Parallel Processor ◽

Simulation Based ◽

Evolution Algorithm

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