Stochastic Number Generators with Minimum Probability Conversion Circuits

2021 ◽  
Author(s):  
Chris Collinsworth ◽  
Sayed Ahmad Salehi
Keyword(s):  
Minimum Probability

scholarly journals A Discrete Imperialist Competitive Algorithm for WSN Deployment

2018 ◽  
Vol 7 (4.1) ◽  
pp. 4
Author(s):  
Kamal Jadidy Aval ◽  
Masumeh Damrudi
Keyword(s):  
Event Detection ◽  
Optimization Problem ◽  
Solution Space ◽  
Imperialist Competitive Algorithm ◽  
Message Delivery ◽  
Exact Methods ◽  
Network Nodes ◽  
Competitive Algorithm ◽  
Minimum Probability ◽  
Np Complete

The WSN deployment problem is addressed in this paper. The problem applies to the monitored areas with different detection needs at different points. In this problem, every point of the terrain is assigned with a predefined minimum probability of event detection. The objective is providing the best position for the network nodes and at the same time assuring event detection, detection message delivery, and reducing deployment cost. We have formulated the problem as an optimization problem with three objectives, which is NP-complete. Because of the huge solution space for the problem and the exponential computational complexity, none of the exact methods known yet can solve the problem unless for a pretty small scaled case. To battle the complexity of the solution, a new scalable solution is proposed based on imperialist competitive algorithm namely imperialist competitive deployment algorithm (ICDA). We compare the proposal to the related deployment strategies, and the results show that ICDA outperforms them.  

A Novel Constraint Specification of Weakly Hard Real-Time System Based on Fixed Sliding Window

2013 ◽  
Vol 475-476 ◽  
pp. 1161-1166
Author(s):  
Xu Dong Zhu ◽  
Hui You Chang
Keyword(s):  
Real Time ◽  
Sliding Window ◽  
The Novel ◽  
Calculation Methods ◽  
Time System ◽  
Real Time System ◽  
Constraint Specification ◽  
Minimum Probability ◽  
System Constraint ◽  
Hard Real Time

This paper puts forward a novel constraint specification. By limitations on the maximum of consecutive miss of deadline and the minimum probability of meeting deadline on the fixed sliding window s×m, the novel constraint specification has three contributions to the research of the weakly hard real-time system constraint specification. Firstly, has configurable flexible parameters which can easily realize all hard real-time, soft real-time and weakly hard real-time only by changing the parameters. Secondly, replacing the "fixed sliding window" with the "any" window, the novel constraint specification not only simplify the calculation of real-time, which will reduce the time consume, but also broaden the application scope such as smoothly scheduling. Moreover, the fixed sliding window is helpful to find a schedulable μ-pattern. Thirdly, the novel constraint specification solves the invalidity of some constraint specification when the first invocation of task is lost or missed. Through analysis and experiments, the results demonstrate that our novel constraint specification outperform previous approaches in both calculation methods and application scopes.

scholarly journals UCAV Path Planning by Fitness-Scaling Adaptive Chaotic Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yudong Zhang ◽  
Lenan Wu ◽  
Shuihua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimal Path ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Minimum Probability ◽  
Chaotic Particle Swarm Optimization

Path planning plays an extremely important role in the design of UCAVs to accomplish the air combat task fleetly and reliably. The planned path should ensure that UCAVs reach the destination along the optimal path with minimum probability of being found and minimal consumed fuel. Traditional methods tend to find local best solutions due to the large search space. In this paper, a Fitness-scaling Adaptive Chaotic Particle Swarm Optimization (FAC-PSO) approach was proposed as a fast and robust approach for the task of path planning of UCAVs. The FAC-PSO employed the fitness-scaling method, the adaptive parameter mechanism, and the chaotic theory. Experiments show that the FAC-PSO is more robust and costs less time than elite genetic algorithm with migration, simulated annealing, and chaotic artificial bee colony. Moreover, the FAC-PSO performs well on the application of dynamic path planning when the threats cruise randomly and on the application of 3D path planning.

scholarly journals An Improved Approximation Algorithm for Co-location Mining in Uncertain Data Sets using Probabilistic Approach

2020 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
M. Sheshikala ◽  
D. Rajeswara Rao ◽  
Md. Ali Kadampur
Keyword(s):  
Possible Worlds ◽  
Demographic Data ◽  
Uncertain Data ◽  
Probabilistic Approach ◽  
Approximation Error ◽  
Computational Time ◽  
Data Sets ◽  
Real World Data ◽  
Minimum Probability ◽  
Prevalence Probability

complete data. Many of the real world data is Uncertain, for example, Demographic data, Sensor networksdata, GIS data etc.,. Handling such data is a challenge for knowledge discovery particularly in colocation mining.One straightforward method is to find the Probabilistic Prevalent colocations (PPCs). This method tries to find allcolocations that are to be generated from a random world. For this we first apply an approximation error to find allthe PPCs which reduce the computations. Next find all the possible worlds and split them into two different worldsand compute the prevalence probability. These worlds are used to compare with a minimum probability threshold todecide whether it is Probabilistic Prevalent colocation (PPCs) or not. The experimental results on the selected dataset show the significant improvement in computational time in comparison to some of the existing methods used incolocation mining.

scholarly journals Government Supervision Mode Selection in the Promotion Period of Prefabricated Construction Using Evolutionary Game

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shuwei Jing ◽  
Zhuangyi Zhang ◽  
Junai Yan
Keyword(s):  
Behavior Pattern ◽  
Mode Selection ◽  
Evolutionary Game ◽  
Practical Case ◽  
Private Interests ◽  
Government Supervision ◽  
Supervision Mode ◽  
Minimum Probability ◽  
The Government ◽  
Speculative Behavior

Aiming at the speculative behavior of some developers who seek private interests in the promotion period of prefabricated construction, this research combines the actual situation, objectively and reasonably determines the parameters in the model, and builds an evolutionary game model to study the choice of government supervision mode in different situations, from the perspective of government supervision. The results showed that the choice of government supervision mode has great connection with the probability of identifying developers’ speculative behavior when the government adopts node supervision. When the probability is greater than the developers’ speculative value, the government will choose node supervision, while the developers will not adopt speculative behavior. Conversely, there will be a periodic behavior pattern in the evolutionary system, and the choice of government supervision mode is related to the value of each parameter. At the same time, the minimum probability of identifying speculative behavior that keeps the optimal situation stable is obtained. On this basis, the paper takes a practical case to discuss the influence of different parameter variations on the choice of government supervision mode and makes numerical simulations; then it puts forward some specific suggestions for government to restrain the speculative behavior of developer.

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