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A textile, embroidered antenna, based on the fractal shape of the Sierpinski triangle, is designed in this paper for operation in the European free Industrial Scientific and Medical (ISM) 863–870 MHz band, as well as in the 902–928 MHz band designated for ISM applications in North and South America. Several prototypes have been fabricated by employing different stitch patterns and thread materials. The effect of the fabrication parameters on the performance of the proposed antenna is investigated through measurements and simulations, with the results being in good agreement. The antenna exhibits attractive characteristics such as wide bandwidth, relatively stable radiation patterns, as well as robustness in washing. Several tests reveal that convex and concave bent conditions do not affect the coverage of the aforementioned ISM bands, despite the shift of the resonant frequency in some cases. Moreover, the SAR values resulting from simulations are below the corresponding thresholds suggested by international guidelines.
Theodoros N. Kapetanakis; Martin Pavec; Melina P. Ioannidou; Christos D. Nikolopoulos; Anargyros T. Baklezos; Radek Soukup; Ioannis O. Vardiambasis. Embroidered Βow-Tie Wearable Antenna for the 868 and 915 MHz ISM Bands. Electronics 2021, 10, 1983 .
AMA StyleTheodoros N. Kapetanakis, Martin Pavec, Melina P. Ioannidou, Christos D. Nikolopoulos, Anargyros T. Baklezos, Radek Soukup, Ioannis O. Vardiambasis. Embroidered Βow-Tie Wearable Antenna for the 868 and 915 MHz ISM Bands. Electronics. 2021; 10 (16):1983.
Chicago/Turabian StyleTheodoros N. Kapetanakis; Martin Pavec; Melina P. Ioannidou; Christos D. Nikolopoulos; Anargyros T. Baklezos; Radek Soukup; Ioannis O. Vardiambasis. 2021. "Embroidered Βow-Tie Wearable Antenna for the 868 and 915 MHz ISM Bands." Electronics 10, no. 16: 1983.
Sewage sludge hydrochars (SSHs), which are produced by hydrothermal carbonization (HTC), offer a high calorific value to be applied as a biofuel. However, HTC is a complex processand the properties of the resulting product depend heavily on the process conditions and feedstock composition. In this work, we have applied artificial neural networks (ANNs) to contribute to the production of tailored SSHs for a specific application and with optimum properties. We collected data from the published literature covering the years 2014–2021, which was then fed into different ANN models where the input data (HTC temperature, process time, and the elemental content of hydrochars) were used to predict output parameters (higher heating value, (HHV) and solid yield (%)). The proposed ANN models were successful in accurately predicting both HHV and contents of C and H. While the model NN1 (based on C, H, O content) exhibited HHV predicting performance with R2 = 0.974, another model, NN2, was also able to predict HHV with R2 = 0.936 using only C and H as input. Moreover, the inverse model of NN3 (based on H, O content, and HHV) could predict C content with an R2 of 0.939.
Theodoros Kapetanakis; Ioannis Vardiambasis; Christos Nikolopoulos; Antonios Konstantaras; Trinh Trang; Duy Khuong; Toshiki Tsubota; Ramazan Keyikoglu; Alireza Khataee; Dimitrios Kalderis. Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge. Energies 2021, 14, 3000 .
AMA StyleTheodoros Kapetanakis, Ioannis Vardiambasis, Christos Nikolopoulos, Antonios Konstantaras, Trinh Trang, Duy Khuong, Toshiki Tsubota, Ramazan Keyikoglu, Alireza Khataee, Dimitrios Kalderis. Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge. Energies. 2021; 14 (11):3000.
Chicago/Turabian StyleTheodoros Kapetanakis; Ioannis Vardiambasis; Christos Nikolopoulos; Antonios Konstantaras; Trinh Trang; Duy Khuong; Toshiki Tsubota; Ramazan Keyikoglu; Alireza Khataee; Dimitrios Kalderis. 2021. "Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge." Energies 14, no. 11: 3000.
The forward and the inverse problem of a thin, circular, loop antenna that radiates in free space is modeled and solved by using soft computing techniques such as artificial neural networks and adaptive neuro fuzzy inference systems. On the one hand, the loop radius and the observation angle serve as inputs to the forward model, whereas the radiation intensity is the output. On the other hand, the electric field intensity and the loop radius are the input and output, respectively, to the inverse model. Extensive numerical tests indicate that the results predicted by the proposed models are in excellent agreement with theoretical data obtained from the existing analytical solutions of the forward problem. Thus, the employment of artificial intelligence techniques for tackling electromagnetic problems turns out to be promising, especially regarding the inverse problems that lack solution with other methods.
Theodoros N. Kapetanakis; Ioannis O. Vardiambasis; Melina P. Ioannidou; Antonios I. Konstantaras. Modeling Antenna Radiation Using Artificial Intelligence Techniques. Electric Vehicles and the Future of Energy Efficient Transportation 2021, 186 -225.
AMA StyleTheodoros N. Kapetanakis, Ioannis O. Vardiambasis, Melina P. Ioannidou, Antonios I. Konstantaras. Modeling Antenna Radiation Using Artificial Intelligence Techniques. Electric Vehicles and the Future of Energy Efficient Transportation. 2021; ():186-225.
Chicago/Turabian StyleTheodoros N. Kapetanakis; Ioannis O. Vardiambasis; Melina P. Ioannidou; Antonios I. Konstantaras. 2021. "Modeling Antenna Radiation Using Artificial Intelligence Techniques." Electric Vehicles and the Future of Energy Efficient Transportation , no. : 186-225.
In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN1, R2 0.897, root mean square error 1.289).
Ioannis O. Vardiambasis; Theodoros N. Kapetanakis; Christos D. Nikolopoulos; Trinh Kieu Trang; Toshiki Tsubota; Ramazan Keyikoglu; Alireza Khataee; Dimitrios Kalderis. Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values. Energies 2020, 13, 4572 .
AMA StyleIoannis O. Vardiambasis, Theodoros N. Kapetanakis, Christos D. Nikolopoulos, Trinh Kieu Trang, Toshiki Tsubota, Ramazan Keyikoglu, Alireza Khataee, Dimitrios Kalderis. Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values. Energies. 2020; 13 (17):4572.
Chicago/Turabian StyleIoannis O. Vardiambasis; Theodoros N. Kapetanakis; Christos D. Nikolopoulos; Trinh Kieu Trang; Toshiki Tsubota; Ramazan Keyikoglu; Alireza Khataee; Dimitrios Kalderis. 2020. "Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values." Energies 13, no. 17: 4572.
Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.
Konstantinos V. Blazakis; Theodoros N. Kapetanakis; George S. Stavrakakis. Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System. Energies 2020, 13, 1 .
AMA StyleKonstantinos V. Blazakis, Theodoros N. Kapetanakis, George S. Stavrakakis. Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System. Energies. 2020; 13 (12):1.
Chicago/Turabian StyleKonstantinos V. Blazakis; Theodoros N. Kapetanakis; George S. Stavrakakis. 2020. "Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System." Energies 13, no. 12: 1.
A modular beamformer, fully scalable in amplitude and phase, to be used with transmitting antenna arrays operating in the Digital Cellular System (DCS)/Personal Communications Service (PCS) frequency band, is designed and fabricated in this paper. The implementation of the structure is simple and very low cost; its performance is evaluated by measuring the frequency response, as well as the radiation patterns produced by an antenna array connected to the output of the beamformer. The latter exhibits low return losses and average amplitude imbalance less than ±0.8 dB, whereas the phase deviations do not exceed ±6° on average, over its 300 MHz bandwidth.
George A. Adamidis; Ioannis O. Vardiambasis; Melina Ioannidou; Theodoros N. Kapetanakis. Design and implementation of an adaptive beamformer for phased array antenna applications. Microwave and Optical Technology Letters 2019, 62, 1780 -1784.
AMA StyleGeorge A. Adamidis, Ioannis O. Vardiambasis, Melina Ioannidou, Theodoros N. Kapetanakis. Design and implementation of an adaptive beamformer for phased array antenna applications. Microwave and Optical Technology Letters. 2019; 62 (4):1780-1784.
Chicago/Turabian StyleGeorge A. Adamidis; Ioannis O. Vardiambasis; Melina Ioannidou; Theodoros N. Kapetanakis. 2019. "Design and implementation of an adaptive beamformer for phased array antenna applications." Microwave and Optical Technology Letters 62, no. 4: 1780-1784.
Soft computing techniques are used, in this paper, to model and solve the inverse problem of a thin, circular, loop antenna that radiates in free space. The electromagnetic field intensity serves as the input to the inverse model, whereas the antenna radius is the output. Three different architectures, based on artificial neural networks (ANNs), are implemented and various training algorithms are tested in order to obtain the optimum performance. The effect of the size of the training data set and the number of the observers on the accuracy of the results are investigated. Specific information for the selection of the appropriate ANN architecture is provided, depending on the constraints imposed by various parameters of the problem. Extensive numerical tests indicate that the results predicted by the proposed models are in excellent agreement with the theoretical data obtained from the existing analytical solutions of the forward problem.
Theodoros N. Kapetanakis; Ioannis O. Vardiambasis; Melina P. Ioannidou; Andreas Maras. Neural Network Modeling for the Solution of the Inverse Loop Antenna Radiation Problem. IEEE Transactions on Antennas and Propagation 2018, 66, 6283 -6290.
AMA StyleTheodoros N. Kapetanakis, Ioannis O. Vardiambasis, Melina P. Ioannidou, Andreas Maras. Neural Network Modeling for the Solution of the Inverse Loop Antenna Radiation Problem. IEEE Transactions on Antennas and Propagation. 2018; 66 (11):6283-6290.
Chicago/Turabian StyleTheodoros N. Kapetanakis; Ioannis O. Vardiambasis; Melina P. Ioannidou; Andreas Maras. 2018. "Neural Network Modeling for the Solution of the Inverse Loop Antenna Radiation Problem." IEEE Transactions on Antennas and Propagation 66, no. 11: 6283-6290.
Analytical methods used to solve the circular loop antenna radiation problem are effective and accurate, but also time-consuming, due to the complex mathematical background. However, soft computing techniques do not require complex mathematical procedures and are more straightforward and fast. In order to solve the circular loop antenna radiation problem, we examine two methods based on artificial intelligence and fuzzy logic. Different neural network learning algorithms are examined, and the fuzzy inference system parameters are identified. Extensive numerical tests show that the predicted values are consistent with those calculated from the analytical techniques. High accuracy and fast convergence make the proposed methods ideal for the prediction of the circular loop antenna characteristics.
Theodoros Nikolaos Kapetanakis; Ioannis O. Vardiambasis; Emmanuel I. Lourakis; Andreas Maras. Applying Neuro-Fuzzy Soft Computing Techniques to the Circular Loop Antenna Radiation Problem. IEEE Antennas and Wireless Propagation Letters 2018, 17, 1673 -1676.
AMA StyleTheodoros Nikolaos Kapetanakis, Ioannis O. Vardiambasis, Emmanuel I. Lourakis, Andreas Maras. Applying Neuro-Fuzzy Soft Computing Techniques to the Circular Loop Antenna Radiation Problem. IEEE Antennas and Wireless Propagation Letters. 2018; 17 (9):1673-1676.
Chicago/Turabian StyleTheodoros Nikolaos Kapetanakis; Ioannis O. Vardiambasis; Emmanuel I. Lourakis; Andreas Maras. 2018. "Applying Neuro-Fuzzy Soft Computing Techniques to the Circular Loop Antenna Radiation Problem." IEEE Antennas and Wireless Propagation Letters 17, no. 9: 1673-1676.
This paper discusses the development of a neural network array model for predicting the radiation performance characteristics of the horn fed parabolic reflector and the dipole fed corner satellite antennas. A number of neural networks were developed in order to predict the radiation characteristics for various combinations of the design parameters. The results obtained from the neural network array models were compared to those from a commercial design software and found in close agreement. The proposed method can predict in less time and with minimum computational resources, the performance characteristics of a horn fed parabolic reflector antenna with high accuracy.
Theodoros N. Kapetanakis; Ioannis O. Vardiambasis. Radiation Performance of Satellite Reflector Antennas Using Neural Networks. 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) 2016, 85 -88.
AMA StyleTheodoros N. Kapetanakis, Ioannis O. Vardiambasis. Radiation Performance of Satellite Reflector Antennas Using Neural Networks. 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI). 2016; ():85-88.
Chicago/Turabian StyleTheodoros N. Kapetanakis; Ioannis O. Vardiambasis. 2016. "Radiation Performance of Satellite Reflector Antennas Using Neural Networks." 2016 Third International Conference on Mathematics and Computers in Sciences and in Industry (MCSI) , no. : 85-88.
The circular cylindrical antenna is a simple, inexpensive, versatile, and very popular antenna type, which has received much attention due its wide range of applications. The exact values of the radiated near and far electromagnetic fields have recently been analytically evaluated, in terms of complex series involving Legendre functions of the second kind and half-integral order. The inverse problem of determining the loop antenna parameters (radius and current) causing specific field levels at one or more points of interest is even more complex. In order to find the solution, avoiding the associated lengthy and time-demanding mathematical analysis, we apply artificial neural network modeling. The proposed models consist of a feedforward back-propagation and a radial basis neural network trained with theoretical data. The results obtained are found to be in perfect agreement with the exact theoretical data.
Theodoros N. Kapetanakis; Ioannis O. Vardiambasis; George S. Liodakis; Andreas Maras. Neural network solution of the circular loop antenna radiation problem. 2012 20th Telecommunications Forum (TELFOR) 2012, 1193 -1196.
AMA StyleTheodoros N. Kapetanakis, Ioannis O. Vardiambasis, George S. Liodakis, Andreas Maras. Neural network solution of the circular loop antenna radiation problem. 2012 20th Telecommunications Forum (TELFOR). 2012; ():1193-1196.
Chicago/Turabian StyleTheodoros N. Kapetanakis; Ioannis O. Vardiambasis; George S. Liodakis; Andreas Maras. 2012. "Neural network solution of the circular loop antenna radiation problem." 2012 20th Telecommunications Forum (TELFOR) , no. : 1193-1196.
This work presents an alternative method, based on artificial techniques, to manipulate the direct and inverse problems of circular loop antenna radiation, using data extracted from their analytical solutions. The adaptive network fuzzy inference system (ANFIS) has been used, as a basis for constructing a set of fuzzy rules with appropriate membership functions in order to obtain the theoretical data. The numerical results for both problems are found to be in excellent agreement with the exact theoretical values.
Theodoros N. Kapetanakis; Ioannis O. Vardiambasis; George S. Liodakis; Andreas Maras. Solving the inverse loop antenna radiation problem using a hybrid neuro-fuzzy system. 2012 20th Telecommunications Forum (TELFOR) 2012, 1189 -1192.
AMA StyleTheodoros N. Kapetanakis, Ioannis O. Vardiambasis, George S. Liodakis, Andreas Maras. Solving the inverse loop antenna radiation problem using a hybrid neuro-fuzzy system. 2012 20th Telecommunications Forum (TELFOR). 2012; ():1189-1192.
Chicago/Turabian StyleTheodoros N. Kapetanakis; Ioannis O. Vardiambasis; George S. Liodakis; Andreas Maras. 2012. "Solving the inverse loop antenna radiation problem using a hybrid neuro-fuzzy system." 2012 20th Telecommunications Forum (TELFOR) , no. : 1189-1192.