scholarly journals Correction to: Industrial-iot-hardware security-improvement using plan load optimization method in cloud

2021 ◽  
Author(s):  
Shakila Basheer ◽  
Magesh Gopu ◽  
Rincy Merlin Mathew ◽  
Maryam Aysha Bivi ◽  
M. Prabu
Keyword(s):  
Hardware Security ◽  
Optimization Method ◽  
Load Optimization ◽  
Industrial Iot

Numerical Simulation of Load Optimization in Multi-Dimension Vibration Test

2013 ◽  
Vol 307 ◽  
pp. 236-239
Author(s):  
Jie Hu ◽  
Huai Yun Zhao
Keyword(s):  
Numerical Simulation ◽  
Equivalence Principle ◽  
Optimization Method ◽  
Shaking Table ◽  
Vibration Test ◽  
Target Response ◽  
Minimum Error ◽  
Numerical Simulation Result ◽  
Load Optimization ◽  
Control Response

This paper introduced a load optimization method in multi-dimension vibration test when the number of shaking table is less than the number of target response. The response equivalence principle is considered as response approximation, the optimization purpose is set as the minimum error between control response and target response, then the load applying in multi-dimension vibration test is analyzed stand at the point of load optimization. Genetic algorithm(GA) is used as the optimization arithmetic, and the numerical simulation result verified the effectiveness of this optimization method. The research of this paper proposed an effective way of calculating the load in multi-dimension vibration test.

Perspective Directions of Artificial Intelligence Systems in Aircraft Load Optimization Process

2020 ◽  
pp. 419-437
Author(s):  
Yelyzaveta Serhiyivna Sahun
Keyword(s):  
Bin Packing ◽  
Three Dimensional ◽  
Optimization Method ◽  
Linear Programs ◽  
Load Optimization ◽  
Load Planning ◽  
Loading Process ◽  
Size Constraints ◽  
Rule Based Approach ◽  
Search Approach

The chapter represents an overview of different approaches towards loading process and load planning. The algorithm and specificities of the current cargo loading process force the scientists to search for new methods of optimizing due to the time, weight, and size constraints of the cargo aircraft and consequently to cut the costs for aircraft load planning and handling procedures. These methods are based on different approaches: mix-integer linear programs, three-dimensional bin packing, knapsack loading algorithms, tabu-search approach, rule-based approach, and heuristics. The perspective direction of aircraft loading process improvement is a combination of multicriteria optimization method and heuristic approach using the expert system.

Optimization Method of Superelevation Transition for Oval Racing Tracks

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Yuchen Wang ◽  
Tao Lu ◽  
Hongxing Zhao ◽  
Zhiying Bao
Keyword(s):  
Optimization Method

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

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