scholarly journals THE EFFICIENCY ESTIMATION OF 900 MHZ RF ENERGY HARVESTER USING ARTIFICIAL NEURAL NETWORK

2017 ◽  
Vol 06 (03) ◽  
pp. 17-23
Author(s):  
Bekir Dursun ◽  
Yunus Uzun ◽  
Erol Kurt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Harvester ◽  
Efficiency Estimation ◽  
Artificial Neural ◽  
Rf Energy ◽  
Rf Energy Harvester

Artificial neural network (ANN)-based optimization of a numerically analyzed m-shaped piezoelectric energy harvester

2021 ◽  
Author(s):  
Ahsan Ali ◽  
Muhammad Abdullah heeraz ◽  
Saira Bibi ◽  
Muhammad Zubair Khan ◽  
Muhammad Sohail Malik ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Energy Harvester ◽  
Research Work ◽  
Electrical Performance ◽  
Optimization Approach ◽  
Maximum Output ◽  
Piezoelectric Energy ◽  
Artificial Neural

In this research work, the M-shaped cantilever piezoelectric energy harvester is modeled and optimized using advanced artificial intelligence algorithms. The proposed harvester adopts a single structure geometrical configuration in which two secondary beams are being connected to the principal bimorph. Finite element analysis is carried out on COMSOL Multiphysics to analyze the efficiency of the proposed energy harvester. The influence of frequency, load resistance, and acceleration on the electrical performance of the harvester is numerically investigated to enhance the bandwidth of the piezoelectric vibrational energy harvester. Numerical analysis is also utilized to obtain the iterative dataset for the training of the artificial neural network. Furthermore, a genetic multi-objective optimization approach is implemented on the trained artificial neural network to obtain the optimal parameters for the proposed energy harvester. It is observed that optimization using modern artificial intelligence approaches implies nonlinearities of the system and therefore, machine learning-based optimization has shown more convincing results, as compared to the traditional statistical methods. Results revealed the maximum output values for the voltage and electrical power are 15.34 V and 4.77 mW at 51.19 Hz, 28.09 k[Formula: see text], and 3.49 g optimal design input parameters. Based on the outcomes, it is recommended to utilize this reliable harvester in low-power micro-devices, electromechanical systems, and smart wearable devices.

Efficiency estimation and reduction potential of the Chinese construction industry via SE-DEA and artificial neural network

2020 ◽  
Vol 27 (7) ◽  
pp. 1533-1552
Author(s):  
Fanning Yuan ◽  
Miaohan Tang ◽  
Jingke Hong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Construction Industry ◽  
Reduction Potential ◽  
Policy Implications ◽  
Content Type ◽  
Efficiency Performance ◽  
Efficiency Estimation ◽  
Industrial Performance ◽  
Artificial Neural

PurposeThe objective of this study is to evaluate the overall technical efficiency, labor efficiency, capital efficiency and equipment efficiency of 30 Chinese construction sectors to foster sustainable economic growth in the construction industry.Design/methodology/approachThis study employed the super-efficiency data envelopment analysis (SE-DEA) and artificial neural network model (ANN) to evaluate the industrial performance and improvement potential of the Chinese regional construction sectors from 2000 to 2017.FindingsResults showed that the overall technical and capital efficiencies displayed relatively stable patterns. Equipment efficiency presented a relatively huge fluctuation during the sample period. Meanwhile, labor, capital and equipment efficiencies could potentially improve in the next five years. A spatial examination of efficiencies implied that the economic level was still a major factor in determining the efficiency performance of the regional construction industry. Beijing, Shanghai and Zhejiang were consistently the leading regions with the best performance in all efficiencies. Shandong and Hubei were critical regions with respect to their large reduction potential of labor, capital and equipment.Research limitations/implicationsThe study focused on the regional efficiency performance of the construction industry; however, it failed to further deeply discover the mechanism that captured the regional inefficiency. In addition, sample datasets used to predict might induce the accuracy of prediction results. Qualitative policy implications failed to regress the efficiency performance of the industrial policy variables. These limitations will be discussed in our further researches.Practical implicationsEnhancing the overall performance of the Chinese construction industry should focus on regions located in the western areas. In comparison with labor and capital efficiencies, equipment efficiency should be given priority by eliminating outdated equipment and developing high technology in the construction industry. In addition, the setting of the national reduction responsibility system should be stratified to account for regional variations.Originality/valueThe findings of this study can provide a systematic understanding for the current and future industry performance of the Chinese construction industry, which would help decision makers to customize appropriate strategies to improve the overall industrial performance with the consideration of regional differences.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

An Artificial Neural Network to Evaluate Cartilage Collagen and Proteoglycan Fractions Based on Multiparametric Quantitative MRI Techniques

2019 ◽  
Author(s):  
Johannes Thüring ◽  
Kevin Linka ◽  
Christiane Kuhl ◽  
Sven Nebelung ◽  
Daniel Truhn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Quantitative Mri ◽  
Artificial Neural

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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