In this work,we introduce a novel Micro Circular Log-Periodic Antenna(MCLPA)optimized with an advanced Evolutionary Neural Network(ENN)algorithm,specifically designed to enhance terahertz(THz)radiation detection.By le...In this work,we introduce a novel Micro Circular Log-Periodic Antenna(MCLPA)optimized with an advanced Evolutionary Neural Network(ENN)algorithm,specifically designed to enhance terahertz(THz)radiation detection.By leveraging the adaptive capabilities of the ENN framework,the antenna design efficiency is significantly improved,enabling rapid prototyping and yielding highly optimized structures tailored for practical THz applications.Extensive characterization confirms that the proposed MCLPA achieves outstanding performance,including an ultra-broad operational bandwidth of 372 GHz(0.135-0.507 THz),a peak gain of 5.51 dBi,an optimal S-parameter(S11)of−13.68 dB,and a maximum radiation efficiency of 82.39%.In addition,the MCLPA exhibits superior sensitivity,low noise susceptibility,and fast response,which are key attributes for reliable and precise THz detection.When configured in array form,the design further enhances gain and directional responsiveness,demonstrating the scalability and deployment potential of the MCLPA.This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering,introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation.By substantially reducing design time and cost while achieving exceptional performance,the proposed ENN framework sets a new benchmark for the development of next-generation THz detection and communication systems,offering broad implications for future high-frequency technologies.展开更多
In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evoluti...In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets.I For this purpose,all of the electrical parameters for six melting processes are measured with a power quality analyzer,attached to the secondary component of an EAF transformer at a Saba steel complex,to estimate the foaming slag quality.Experimental results on various combinations of measured electrical parameters,applying the designed EWNN estimator,demonstrate that utilizing five leading indicators leads to the highest precision.The obtained 99%accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.展开更多
Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimizat...Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem.ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions.However,ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays(FPGAs)and graphic processing units(GPUs)to achieve a good performance.This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing.Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches.Real data form mass spectrometry data(MSD)application was tested to examine and verify our implementations.This is a very important and extensive computation application which needs to search and find the optimal features(peaks)in MSD in order to distinguish cancer patients from control patients.ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes.The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6,respectively.展开更多
Lacking labeled examples of working numerical strategies,adapting an iterative solver to accommodate a numerical issue,e.g.,density discontinuities in the pressure Poisson equation,is non-trivial and usually involves ...Lacking labeled examples of working numerical strategies,adapting an iterative solver to accommodate a numerical issue,e.g.,density discontinuities in the pressure Poisson equation,is non-trivial and usually involves a lot of trial and error.Here,we resort to evolutionary neural network.A evolutionary neural network observes the outcome of an action and adapts its strategy accordingly.The process requires no labeled data but only a measure of a network’s performance at a task.Applying neuro-evolution and adapting the Jacobi iterative method for the pressure Poisson equation with density discontinuities,we show that the adapted Jacobi method is able to accommodate density discontinuities.展开更多
基金support from the Natural Sciences and Engineering Research Council of Canada(NSERC)and the Micro-Nano Technology(MNT)program facilitated by CMC Microsystems.
文摘In this work,we introduce a novel Micro Circular Log-Periodic Antenna(MCLPA)optimized with an advanced Evolutionary Neural Network(ENN)algorithm,specifically designed to enhance terahertz(THz)radiation detection.By leveraging the adaptive capabilities of the ENN framework,the antenna design efficiency is significantly improved,enabling rapid prototyping and yielding highly optimized structures tailored for practical THz applications.Extensive characterization confirms that the proposed MCLPA achieves outstanding performance,including an ultra-broad operational bandwidth of 372 GHz(0.135-0.507 THz),a peak gain of 5.51 dBi,an optimal S-parameter(S11)of−13.68 dB,and a maximum radiation efficiency of 82.39%.In addition,the MCLPA exhibits superior sensitivity,low noise susceptibility,and fast response,which are key attributes for reliable and precise THz detection.When configured in array form,the design further enhances gain and directional responsiveness,demonstrating the scalability and deployment potential of the MCLPA.This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering,introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation.By substantially reducing design time and cost while achieving exceptional performance,the proposed ENN framework sets a new benchmark for the development of next-generation THz detection and communication systems,offering broad implications for future high-frequency technologies.
文摘In the present study,a novel approach based on an evolutionary wavelet neural network(EWNN)is proposed to estimate the slag quality in an electric arc furnace(EAF)employing power quality indices.In the EWNN,an evolutionary method is applied to train the parameters for a combination of neural networks and wavelets.I For this purpose,all of the electrical parameters for six melting processes are measured with a power quality analyzer,attached to the secondary component of an EAF transformer at a Saba steel complex,to estimate the foaming slag quality.Experimental results on various combinations of measured electrical parameters,applying the designed EWNN estimator,demonstrate that utilizing five leading indicators leads to the highest precision.The obtained 99%accuracy for estimating the foaming slag quality by EWNN compared to the other methods illustrates the proposed method's efficiency.
文摘Evolutionary neural network(ENN)shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces.It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem.ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions.However,ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays(FPGAs)and graphic processing units(GPUs)to achieve a good performance.This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing.Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches.Real data form mass spectrometry data(MSD)application was tested to examine and verify our implementations.This is a very important and extensive computation application which needs to search and find the optimal features(peaks)in MSD in order to distinguish cancer patients from control patients.ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes.The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6,respectively.
基金Shi acknowledges financial support from the National Natural Science Foundation of China(Grant 91752202).
文摘Lacking labeled examples of working numerical strategies,adapting an iterative solver to accommodate a numerical issue,e.g.,density discontinuities in the pressure Poisson equation,is non-trivial and usually involves a lot of trial and error.Here,we resort to evolutionary neural network.A evolutionary neural network observes the outcome of an action and adapts its strategy accordingly.The process requires no labeled data but only a measure of a network’s performance at a task.Applying neuro-evolution and adapting the Jacobi iterative method for the pressure Poisson equation with density discontinuities,we show that the adapted Jacobi method is able to accommodate density discontinuities.
