An investigation on the effective performance area of the electric bicycle with variable key input parameters

2021 ◽  
Vol 321 ◽  
pp. 128862
Author(s):  
Le Trong Hieu ◽  
Nguyen Xuan Khoa ◽  
Ock Teack Lim
Keyword(s):  
Input Parameters ◽  
Effective Performance ◽  
Electric Bicycle ◽  
Performance Area

Optimization of wireless sensor network deployment for electromagnetic situation monitoring

2018 ◽  
Vol 10 (7) ◽  
pp. 746-753 ◽  
Author(s):  
Krzysztof Malon ◽  
Paweł Skokowski ◽  
Jerzy Lopatka
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Urban Areas ◽  
Environmental Parameters ◽  
Probability Of Detection ◽  
Wireless Sensor ◽  
Electromagnetic Spectrum ◽  
Radio Receivers ◽  
Effective Performance ◽  
Performance Area

Wireless sensor networks are an increasingly popular tool for monitoring various environmental parameters. They can also be used for monitoring the electromagnetic spectrum. Wireless sensors, due to their small size, typically have simplified radio receivers with reduced sensitivity and use small antennas. As a result, their effective performance area is similarly limited. This is especially important in urban areas where there are various kinds of adverse propagation phenomena related to area coverage. The aim of this paper is to present the phenomena in the wireless sensor networks and propose criteria and methods to optimize their deployment to ensure maximizing the probability of detection of emissions, minimization of unmonitored areas, and to provide the necessary hardware redundancy in the priority areas. Influence of detection parameters, number of sensors and range constraints between sensors on received outcomes are also presented.

A study of the effects of input parameters on the dynamics and required power of an electric bicycle

2017 ◽  
Vol 204 ◽  
pp. 1347-1362 ◽  
Author(s):  
Nguyen Ba Hung ◽  
Sung Jaewon ◽  
Ocktaeck Lim
Keyword(s):  
Input Parameters ◽  
Electric Bicycle

Effective performance improvement of organic thin film transistors with multi-layer modifications

2020 ◽  
Vol 91 (3) ◽  
pp. 30201
Author(s):  
Hang Yu ◽  
Jianlin Zhou ◽  
Yuanyuan Hao ◽  
Yao Ni
Keyword(s):  
Thin Film ◽  
Thin Film Transistors ◽  
Performance Enhancement ◽  
Organic Thin Film Transistors ◽  
Electrical Performance ◽  
Hole Injection ◽  
Organic Thin Film ◽  
Effective Performance ◽  
Significant Performance ◽  
P Type

Organic thin film transistors (OTFTs) based on dioctylbenzothienobenzothiophene (C8BTBT) and copper (Cu) electrodes were fabricated. For improving the electrical performance of the original devices, the different modifications were attempted to insert in three different positions including semiconductor/electrode interface, semiconductor bulk inside and semiconductor/insulator interface. In detail, 4,4′,4′′-tris[3-methylpheny(phenyl)amino] triphenylamine (m-MTDATA) was applied between C8BTBTand Cu electrodes as hole injection layer (HIL). Moreover, the fluorinated copper phthalo-cyanine (F16CuPc) was inserted in C8BTBT/SiO2 interface to form F16CuPc/C8BTBT heterojunction or C8BTBT bulk to form C8BTBT/F16CuPc/C8BTBT sandwich configuration. Our experiment shows that, the sandwich structured OTFTs have a significant performance enhancement when appropriate thickness modification is chosen, comparing with original C8BTBT devices. Then, even the low work function metal Cu was applied, a normal p-type operate-mode C8BTBT-OTFT with mobility as high as 2.56 cm2/Vs has been fabricated.

Fuzzy Sets Theory in Comparison with Stochastic Methods to Analyse Nonlinear Behaviour of a Steel Member under Compression

2005 ◽  
Vol 10 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Z. Kala
Keyword(s):  
Fuzzy Sets ◽  
Carrying Capacity ◽  
Stochastic Methods ◽  
Nonlinear Behaviour ◽  
Load Carrying Capacity ◽  
Load Carrying ◽  
Input Parameters ◽  
Stochastic Solution ◽  
Fuzzy Solution ◽  
Sets Theory

The load-carrying capacity of the member with imperfections under axial compression is analysed in the present paper. The study is divided into two parts: (i) in the first one, the input parameters are considered to be random numbers (with distribution of probability functions obtained from experimental results and/or tolerance standard), while (ii) in the other one, the input parameters are considered to be fuzzy numbers (with membership functions). The load-carrying capacity was calculated by geometrical nonlinear solution of a beam by means of the finite element method. In the case (ii), the membership function was determined by applying the fuzzy sets, whereas in the case (i), the distribution probability function of load-carrying capacity was determined. For (i) stochastic solution, the numerical simulation Monte Carlo method was applied, whereas for (ii) fuzzy solution, the method of the so-called α cuts was applied. The design load-carrying capacity was determined according to the EC3 and EN1990 standards. The results of the fuzzy, stochastic and deterministic analyses are compared in the concluding part of the paper.

A Hybrid Approach for Virtual Machine Migration in Cloud Computing Environment

2017 ◽  
Vol 7 (9) ◽  
pp. 30 ◽  
Author(s):  
Ramandeep Kaur
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Heuristic Algorithms ◽  
Hybrid Approach ◽  
Cloud Infrastructure ◽  
Virtual Machine Migration ◽  
Bee Colony ◽  
Effective Manner ◽  
Effective Performance

A lot of research has been done in the field of cloud computing in computing domain.  For its effective performance, variety of algorithms has been proposed. The role of virtualization is significant and its performance is dependent on VM Migration and allocation. More of the energy is absorbed in cloud; therefore, the utilization of numerous algorithms is required for saving energy and efficiency enhancement in the proposed work. In the proposed work, green algorithm has been considered with meta heuristic algorithms, ABC (Artificial Bee colony .Every server has to perform different or same functions. A cloud computing infrastructure can be modelled as Primary Machineas a set of physical Servers/host PM1, PM2, PM3… PMn. The resources of cloud infrastructure can be used by the virtualization technology, which allows one to create several VMs on a physical server or host and therefore, lessens the hardware amount and enhances the resource utilization. The computing resource/node in cloud is used through the virtual machine. To address this problem, data centre resources have to be managed in resource -effective manner for driving Green Cloud computing that has been proposed in this work using Virtual machine concept with ABC and Neural Network optimization algorithm. The simulations have been carried out in CLOUDSIM environment and the parameters like SLA violations, Energy consumption and VM migrations along with their comparison with existing techniques will be performed.

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