Current Issue : October - December Volume : 2020 Issue Number : 4 Articles : 5 Articles
This paper proposes a new approach for load balancing by using receiver-\ninitiated load transfer method. Usually data transfer for load balancing\nstarts from a sender node. This is what called a sender-initiated method. In\nthis paper, instead, a load balancing action starts from a receiver node; the\nreceiver node initiates load balancing for asking a sender node for load transfer.\nFuzzy logic control is applied in this approach to avoid using a fixed\nthreshold value in load balancing in ad-hoc networks. Performance for the\nreceiver-initiated approach is evaluated and compared with other load balancing\napproaches-BID protocol, fuzzy logic sender-initiated algorithm and\nnon-fuzzy (threshold based) receiver-initiated algorithm. The results show\nthat the receiver-initiated approach improves network performance by comparing\nwith the other three....
Electric direct-current (DC) drives based on DC motor are extremely important in the manufacturing process, so it must be crucial to increase their performance when they are working on load disturbances or the DC motorâ??s parameters change. Usually, several load torque suddenly appears when electric drives are operating in a speed closed-loop, so robust controllers are required to keep the speed high-performance. One of the most well-known robust strategies is the sliding mode controller (SMC), which works under discontinue operation. This controller can handle disturbances and variations in the plantâ??s parameters, so the controller has robust performance. Nevertheless, it has some disadvantages (chattering). Therefore, this paper proposed a fuzzy logic controller (FLC) that includes an artificial organic network for adjusting the command signal of the SMC. The proposed controller gives a smooth signal that decrements the chattering in the SMC. The stability condition that is based on Lyapunov of the DC motor is driven is evaluated; besides, the stability margins are calculated. The proposed controller is designed using co-simulation and a real testbed since co-simulation is an extremely useful tool in academia and industry allows to move from co-simulation to real implementation in short period of time. Moreover, there are several universities and industries that adopt co-simulation as the main step to design prototypes. Thus, engineering students and designers are able to achieve excellent results when they design rapid and functional prototypes. For instance, co-simulation based on Multisim leads to design directly printed circuit boards so engineering students or designers could swiftly get an experimental DC drive. The experimental results using this platform show excellent DC-drive performance when the load torque disturbances are suddenly applied to the system. As a result, the proposed controller based on fuzzy artificial organic and SMC allows for adjusting the command signal that improves the dynamic response in DC drives. The experimental response using the sliding-mode controller with fuzzy artificial organic networks is compared against an auto-tuning, Proportional-Integral-Derivative (PID), which is a conventional controller. The PID controller is the most implemented controller in several industries, so this proposal can contribute to improving manufacturing applications, such as micro-computer numerical control (CNC) machines. Moreover, the proposed robust controller achieves a superior-speed response under the whole tested scenarios. Finally, the presented design methodology based on co-simulation could be used by universities and industry for validating and implementing advanced control systems in DC drives....
In handing information regarding various aspects of uncertainty, non-classicalmathematics\n(fuzzy mathematics or great extension and development of classical\nmathematics) is considered to be a more powerful technique than classical\nmathematics. The non-classical mathematics, therefore, has now days\nbecome a useful tool in applications mathematics and computer science. The\npurpose of this paper is to apply the concept of the fuzzy sets to some algebraic\nstructures such as an ideal, upper semilattice, lower semilattice, lattice\nand sub-algebra and gives some properties of these algebraic structures by\nusing the concept of fuzzy sets. Finally, related properties are investigated in\nfuzzy BCK-algebras....
Lithium ion (Li-Ion) and lithium polymer (Li-Po) batteries need to be used within certain\nvoltage/current limits. Failure to observe these limits may result in damage to the battery. In this\nwork, we propose a low voltage battery management system (LV-BMS) that balances the processes\nof the battery cells in the battery pack and the activating-deactivating of cells by guaranteeing that\nthe operation is within these limits. The system operates autonomously and provides energy from\nthe internal battery. It has a modular structure and the software is designed to control the charging\nand discharging of eight battery cells at most. A STM32F103 microcontroller is used for system\ncontrol. The fuzzy logic controller (FLC) is used to set the discharge voltage limit to prevent damage\nto the battery cells, shorten the settlement time and create a specialized design for charge control.\nThe proposed structure enables solar panel or power supplies with different voltage values between\n5 V and 8 V to be used for charging. The experimental results show there was a 42% increase in\nusage time and the voltage difference between the batteries was limited to a maximum of 65 mV.\nMoreover, the charge current settles at about 20 ms, which is a much faster response when compared\nto a PID controller....
Risk evaluation is an effective way to reduce the impacts of natural hazards\nand it plays an increasingly important role in emergency management. Traditional\nmethods of assessing risks mainly utilize Geographic Information System\n(GIS) to get risk map, and information diffusion method (IDM) to deal\nwith incomplete data sets. However, there are few papers discuss the uncertainty\nof integrated hazards and consider dynamic risk under time dimension.\nThe model proposed in this study combines the variable fuzzy set theory\nwith information diffusion method (VFS-IDM) to solve the uncertainness of\nmultiple hazards dynamic risk assessment when data sets are incomplete.\nThis study employs fuzzy set theory (VFS) to calculate the relative membership\ndegree and applies information entropy method (IEM) to obtain the\nweights of criteria indicators for multiple hazards evaluation. Then applies\ninformation diffusion method (IDM) to estimate condition probability distribution\nand vulnerability curve with the VFS-IEM model results, time data\nand multiple hazards losses. Then the expected value of multiple hazards dynamic\nrisk can be calculated by using the normal information diffusion estimator\nso as to improve the accuracy of risk evaluation results....
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