Harmonics mitigation based on the minimization of non-linearity current in a power system

Harmonic issues in power systems are becoming an important topic for industrial customers and power suppliers alike due to their adverse effects in both consumer appliances as well as for utility suppliers. Consumers should seek to reduce harmonic pollution, regardless of voltage or current distortion already present in the network. This article suggests a new method for suppressing distortions by using the non-linearity current index (NLCI) to determine the shunt single-tuned passive filter (STPF) compensator value in non-sinusoidal power systems, with the objective of maintaining the power factor within desired limits. The objective of the proposed method is to minimize the nonlinear current of customer’s loads in the power system at the point of common coupling (PCC). Moreover, the proposed design takes into consideration other practical constraints for the total voltage and individual harmonic distortion limits, ensuring compliance with (Institute of Electrical and Electronics Engineers) IEEE 519-2014 guidelines, maintaining distortions at an acceptable level while also abiding by the capacitor loading constraints established in IEEE 18-2012. The performance of the optimally designed compensator is assessed using well documented IEEE standards based on numerical examples of nonlinear loads taken from previous publications

On the interpretation of responses from hydrogel based distributed microbend fibre optic sensors

This contribution discuses the physicochemical aspects associated with the response of hydrogel based distributed fibre optic microbend sensors to different humidity conditions. We explain that the swelling of the hydrogel which leads to the observed change in the OTDR signal should be attributed to a change in the water potential of the hydrogel being at an equilibrium with the water potential of its immediate physicochemical environment. Since the water potential in the hydrogel matrix is the result of several equilibration processes from multiple species that are interacting in the immediate environment surrounding the sensor, the observed fibre deformation should be attributed to all of the components of the chemical potential. The work draws attention to the necessity to fully characterize the hydrogel system used in each sensing application. The analysis is of relevance to all types of fibre optic biosensors that utilize hydrogels in the measurement process

On the interpretation of responses from hydrogel based distributed microbend fibre optic sensors

This contribution discuses the physicochemical aspects associated with the response of hydrogel based distributed fibre optic microbend sensors to different humidity conditions. We explain that the swelling of the hydrogel which leads to the observed change in the OTDR signal should be attributed to a change in the water potential of the hydrogel being at an equilibrium with the water potential of its immediate physicochemical environment. Since the water potential in the hydrogel matrix is the result of several equilibration processes from multiple species that are interacting in the immediate environment surrounding the sensor, the observed fibre deformation should be attributed to all of the components of the chemical potential. The work draws attention to the necessity to fully characterize the hydrogel system used in each sensing application. The analysis is of relevance to all types of fibre optic biosensors that utilize hydrogels in the measurement process

Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers

This work provides a performance comparison of four different machine learning classifiers: multinomial logistic regression with ridge estimators (MLR) classifier, k-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) as applied to terahertz (THz) transient time domain sequences associated with pixelated images of different powder samples. The six substances considered, although have similar optical properties, their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties. As scattering can be unquantifiable in many spectroscopic experiments, classification solely on differences in complex insertion loss can be inconclusive. The problem is addressed using two-dimensional (2-D) cross-correlations between background and sample interferograms, these ensure good noise suppression of the datasets and provide a range of statistical features that are subsequently used as inputs to the above classifiers. A cross-validation procedure is adopted to assess the performance of the classifiers. Firstly the measurements related to samples that had thicknesses of 2 mm were classified, then samples at thicknesses of 4 mm, and after that 3 mm were classified and the success rate and consistency of each classifier was recorded. In addition, mixtures having thicknesses of 2 and 4 mm as well as mixtures of 2, 3 and 4 mm were presented simultaneously to all classifiers. This approach provided further cross-validation of the classification consistency of each algorithm. The results confirm the superiority in classification accuracy and robustness of the MLR (least accuracy 88.24%) and KNN (least accuracy 90.19%) algorithms which consistently outperformed the SVM (least accuracy 74.51%) and NB (least accuracy 56.86%) classifiers for the same number of feature vectors across all studies. The work establishes a general methodology for assessing the performance of other hyperspectral dataset classifiers on the basis of 2-D cross-correlations in far-infrared spectroscopy or other parts of the electromagnetic spectrum. It also advances the wider proliferation of automated THz imaging systems across new application areas e.g., biomedical imaging, industrial processing and quality control where interpretation of hyperspectral images is still under development

A robust controller design method for feedback substitution schemes using genetic algorithms

Controllers for feedback substitution schemes demonstrate a trade-off between noise power gain and normalized response time. Using as an example the design of a controller for a radiometric transduction process subjected to arbitrary noise power gain and robustness constraints, a Pareto-front of optimal controller solutions fulfilling a range of time-domain design objectives can be derived. In this work, we consider designs using a loop shaping design procedure (LSDP). The approach uses linear matrix inequalities to specify a range of objectives and a genetic algorithm (GA) to perform a multi-objective optimization for the controller weights (MOGA). A clonal selection algorithm is used to further provide a directed search of the GA towards the Pareto front. We demonstrate that with the proposed methodology, it is possible to design higher order controllers with superior performance in terms of response time, noise power gain and robustness

Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

New opportunities for secure communication networks using shaped femtosecond laser pulses inducing filamentation processes in the atmosphere

The current study discusses new opportunities for secure ground to satellite communications using shaped femtosecond pulses that induce spatial hole burning in the atmosphere for efficient communications with data encoded within super-continua generated by femtosecond pulses. Refractive index variation across the different layers in the atmosphere may be modelled using assumptions that the upper strata of the atmosphere and troposphere behaving as layered composite amorphous dielectric networks composed of resistors and capacitors with different time constants across each layer. Input-output expressions of the dynamics of the networks in the frequency domain provide the transmission characteristics of the propagation medium. Femtosecond pulse shaping may be used to optimize the pulse phase-front and spectral composition across the different layers in the atmosphere. A generic procedure based on evolutionary algorithms to perform the pulse shaping is proposed. In contrast to alternative procedures that would require ab initio modelling and calculations of the propagation constant for the pulse through the atmosphere, the proposed approach is adaptive, compensating for refractive index variations along the column of air between the transmitter and receiver

New opportunities for secure communication networks using shaped femtosecond laser pulses inducing filamentation processes in the atmosphere

The current study discusses new opportunities for secure ground to satellite communications using shaped femtosecond pulses that induce spatial hole burning in the atmosphere for efficient communications with data encoded within super-continua generated by femtosecond pulses. Refractive index variation across the different layers in the atmosphere may be modelled using assumptions that the upper strata of the atmosphere and troposphere behaving as layered composite amorphous dielectric networks composed of resistors and capacitors with different time constants across each layer. Input-output expressions of the dynamics of the networks in the frequency domain provide the transmission characteristics of the propagation medium. Femtosecond pulse shaping may be used to optimize the pulse phase-front and spectral composition across the different layers in the atmosphere. A generic procedure based on evolutionary algorithms to perform the pulse shaping is proposed. In contrast to alternative procedures that would require ab initio modelling and calculations of the propagation constant for the pulse through the atmosphere, the proposed approach is adaptive, compensating for refractive index variations along the column of air between the transmitter and receiver

Propagation of errors from a null balance terahertz reflectometer to a sample's relative water content

The THz water content index of a sample is defined and advantages in using such metric in estimating a sample's relative water content are discussed. The errors from reflectance measurements performed at two different THz frequencies using a quasi-optical null-balance reflectometer are propagated to the errors in estimating the sample water content index