Journal articles on the topic 'Waveform generation algorithms'
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Oliveira, Andrey Augusto Alves de, Jorge Antonio Silva Centeno, and Fabiano Scheer Hainosz. "POINT CLOUD GENERATION FROM GAUSSIAN DECOMPOSITION OF THE WAVEFORM LASER SIGNAL WITH GENETIC ALGORITHMS." Boletim de Ciências Geodésicas 24, no. 2 (June 2018): 270–87. http://dx.doi.org/10.1590/s1982-21702018000200018.
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Abstract: Recent developments in LIDAR technology lead to the availability of the waveform systems, which capture and digitize the whole return of the emitted LASER pulse. As many objects may cause multiple returns in the same echo, one task is to detect and separate different echoes within the same digitized measurement. In this paper the results of a study aimed at LASER signal waveform decomposition using genetic algorithms are introduced. The proposed method is based on the Gaussian decomposition approach and analyzes each digitized return to compute one or more points. Initially, the number of peaks contained in the waveform is determined by a simple peak detection method, with a local maximum point algorithm. When more than one peak is detected, genetic algorithms are applied to estimate the amplitude, time and standard deviation of each peak within the digitized signal. With this methodology it was possible to increase the number of points by approximately 17 % compared to the point cloud obtained using commercial software. The best results were obtained in areas with high vegetation, and thus the methodology can be applied to the generation of denser points cloud in forest areas.
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Sui, Jingkun, Qingcai Zeng, Zhifang Yang, Xiaodong Zheng, and Tianyue Hu. "Amplitude semblance and its fusion with the third-generation coherence for characterization of fractured-vuggy carbonate reservoirs." Journal of Geophysics and Engineering 19, no. 5 (September 10, 2022): 1005–11. http://dx.doi.org/10.1093/jge/gxac061.
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Abstract In the Tarim Basin, the main hydrocarbon reservoirs of Ordovician carbonate rocks are fractured-vuggy reservoirs, of which the underground river type reservoirs are an important type. Seismic coherence attribute can highlight seismic discontinuity caused by tectonic movements, reservoir boundaries, sedimentary body boundaries or other factors. Thus, it is a widely used key technique in seismic interpretation. There are many algorithms to determine the coherence. Typically, the coherence algorithm based on eigen-structure analysis is the most robust, but is sensitive to waveform differences and insensitive to amplitude differences. This paper proposes an amplitude coherence attribute to measure semblance of root-mean-square (RMS) amplitudes of multiple traces and fuses it with the third-generation coherence (C3) to describe the boundary of underground river. Model test and case study prove that the proposed fused algorithm can effectively identify the amplitude and waveform differences in seismic data.
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Younis, Raneen, and Andreas Reinhardt. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures." Energies 13, no. 12 (June 12, 2020): 3039. http://dx.doi.org/10.3390/en13123039.
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The number of globally deployed smart meters is rising, and so are the sampling rates at which they can meter electrical consumption data. As a consequence thereof, the technological foundation is established to track the power intake of buildings at sampling rates up to several k Hz . Processing raw signal waveforms at such rates, however, imposes a high resource demand on the metering devices and data processing algorithms alike. In fact, the ensuing resource demand often exceeds the capabilities of the embedded systems present in current-generation smart meters. Consequently, the majority of today’s energy data processing algorithms are confined to the use of RMS values of the data instead, reported once per second or even less frequently. This entirely eliminates the spectral characteristics of the signal waveform (i.e., waveform trajectories of electrical voltage, current, or power) from the data, despite the wealth of information they have been shown to contain about the operational states of the operative appliances. In order to overcome this limitation, we pursue a novel approach to handle the ensuing volume of load signature data and simultaneously facilitate their analysis. Our proposed method is based on approximating the current intake of electrical appliances by means of parametric models, the determination of whose parameters only requires little computational power. Through the identification of model parameters from raw measurements, smart meters not only need to transmit less data, but the identification of individual loads in aggregate load signature data is facilitated at the same time. We conduct an analysis of the fundamental waveform shapes prevalent in the electrical power consumption data of more than 50 electrical appliances, and assess the induced approximation errors when replacing raw current consumption data by parametric models. Our results show that the current consumption of many household appliances can be accurately modeled by a small number of parameterizable waveforms.
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Witte, Philipp A., Mathias Louboutin, Navjot Kukreja, Fabio Luporini, Michael Lange, Gerard J. Gorman, and Felix J. Herrmann. "A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia." GEOPHYSICS 84, no. 3 (May 1, 2019): F57—F71. http://dx.doi.org/10.1190/geo2018-0174.1.
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Writing software packages for seismic inversion is a very challenging task because problems such as full-waveform inversion or least-squares imaging are algorithmically and computationally demanding due to the large number of unknown parameters and the fact that waves are propagated over many wavelengths. Therefore, software frameworks need to combine versatility and performance to provide geophysicists with the means and flexibility to implement complex algorithms that scale to exceedingly large 3D problems. Following these principles, we have developed the Julia Devito Inversion framework, an open-source software package in Julia for large-scale seismic modeling and inversion based on Devito, a domain-specific language compiler for automatic code generation. The framework consists of matrix-free linear operators for implementing seismic inversion algorithms that closely resemble the mathematical notation, a flexible resilient parallelization, and an interface to Devito for generating optimized stencil code to solve the underlying wave equations. In comparison with many manually optimized industry codes written in low-level languages, our software is built on the idea of independent layers of abstractions and user interfaces with symbolic operators. Through a series of numerical examples, we determined that this allows users to implement a series of increasingly complex algorithms for waveform inversion and imaging as simple Julia scripts that scale to large-scale 3D problems. This illustrates that software based on the paradigms of abstract user interfaces and automatic code generation and makes it possible to manage the complexity of the algorithms and performance optimizations, thus providing a high-performance research and production framework.
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Stoffa, Paul L., and Mrinal K. Sen. "Nonlinear multiparameter optimization using genetic algorithms: Inversion of plane‐wave seismograms." GEOPHYSICS 56, no. 11 (November 1991): 1794–810. http://dx.doi.org/10.1190/1.1442992.
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Seismic waveform inversion is one of many geophysical problems which can be identified as a nonlinear multiparameter optimization problem. Methods based on local linearization fail if the starting model is too far from the true model. We have investigated the applicability of “Genetic Algorithms” (GA) to the inversion of plane‐wave seismograms. Like simulated annealing, genetic algorithms use a random walk in model space and a transition probability rule to help guide their search. However, unlike a single simulated annealing run, the genetic algorithms search from a randomly chosen population of models (strings) and work with a binary coding of the model parameter set. Unlike a pure random search, such as in a “Monte Carlo” method, the search used in genetic algorithms is not directionless. Genetic algorithms essentially consist of three operations, selection, crossover, and mutation, which involve random number generation, string copies, and some partial string exchanges. The choice of the initial population, the probabilities of crossover and mutation are crucial for the practical implementation of the algorithm. We investigated the effects of these parameters in the inversion of plane‐wave seismograms in which a normalized crosscorrelation function was used as the objective or fitness function (E). We also introduce the concept of “update” probability to control the influence of past generations. The combination of a low value of mutation probability (∼0.01), a moderate value of the crossover probability (∼0.6) and a high value of update probability (∼0.9) are found to be optimal for the convergence of the algorithm. Further, we show that concepts from simulated annealing can be used effectively for the stretching of the fitness function which helps in the convergence of the algorithm. Thus, we propose to use exp (E/T) rather than E as the fitness function, where T (analogous to temperature in simulated annealing) is a properly chosen parameter which can change slowly with each generation. Also, by repeating the GA optimization procedure several times with different randomly chosen initial model populations, we derive “a very good subset” of models from the entire model space and calculate the a posteriori probability density σ(m) ∝ exp (E(m)/T). The σ(m) ’s are then used to calculate a “mean” model, which is found to be close to the true model.
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Bellan, Diego, and Sergio A. Pignari. "Statistical Properties of Real–Time Amplitude Estimate of Harmonics Affected by Frequency Instability." Journal of Electrical Engineering 67, no. 4 (July 1, 2016): 292–98. http://dx.doi.org/10.1515/jee-2016-0043.
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Abstract This work deals with the statistical characterization of real-time digital measurement of the amplitude of harmonics affected by frequency instability. In fact, in modern power systems both the presence of harmonics and frequency instability are well-known and widespread phenomena mainly due to nonlinear loads and distributed generation, respectively. As a result, real-time monitoring of voltage/current frequency spectra is of paramount importance as far as power quality issues are addressed. Within this framework, a key point is that in many cases real-time continuous monitoring prevents the application of sophisticated algorithms to extract all the information from the digitized waveforms because of the required computational burden. In those cases only simple evaluations such as peak search of discrete Fourier transform are implemented. It is well known, however, that a slight change in waveform frequency results in lack of sampling synchronism and uncertainty in amplitude estimate. Of course the impact of this phenomenon increases with the order of the harmonic to be measured. In this paper an approximate analytical approach is proposed in order to describe the statistical properties of the measured magnitude of harmonics affected by frequency instability. By providing a simplified description of the frequency behavior of the windows used against spectral leakage, analytical expressions for mean value, variance, cumulative distribution function, and probability density function of the measured harmonics magnitude are derived in closed form as functions of waveform frequency treated as a random variable.
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Spurio Mancini, Alessio, Davide Piras, Ana Margarida Godinho Ferreira, Michael Paul Hobson, and Benjamin Joachimi. "Accelerating Bayesian microseismic event location with deep learning." Solid Earth 12, no. 7 (July 29, 2021): 1683–705. http://dx.doi.org/10.5194/se-12-1683-2021.
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Abstract. We present a series of new open-source deep-learning algorithms to accelerate Bayesian full-waveform point source inversion of microseismic events. Inferring the joint posterior probability distribution of moment tensor components and source location is key for rigorous uncertainty quantification. However, the inference process requires forward modelling of microseismic traces for each set of parameters explored by the sampling algorithm, which makes the inference very computationally intensive. In this paper we focus on accelerating this process by training deep-learning models to learn the mapping between source location and seismic traces for a given 3D heterogeneous velocity model and a fixed isotropic moment tensor for the sources. These trained emulators replace the expensive solution of the elastic wave equation in the inference process. We compare our results with a previous study that used emulators based on Gaussian processes to invert microseismic events. For fairness of comparison, we train our emulators on the same microseismic traces and using the same geophysical setting. We show that all of our models provide more accurate predictions, ∼ 100 times faster predictions than the method based on Gaussian processes, and a 𝒪(105) speed-up factor over a pseudo-spectral method for waveform generation. For example, a 2 s long synthetic trace can be generated in ∼ 10 ms on a common laptop processor, instead of ∼ 1 h using a pseudo-spectral method on a high-profile graphics processing unit card. We also show that our inference results are in excellent agreement with those obtained from traditional location methods based on travel time estimates. The speed, accuracy, and scalability of our open-source deep-learning models pave the way for extensions of these emulators to generic source mechanisms and application to joint Bayesian inversion of moment tensor components and source location using full waveforms.
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Aboltins, Arturs, Dmitrijs Pikulins, Juris Grizans, and Sergejs Tjukovs. "Piscivorous Bird Deterrent Device Based on a Direct Digital Synthesis of Acoustic Signals." Elektronika ir Elektrotechnika 27, no. 6 (December 14, 2021): 42–48. http://dx.doi.org/10.5755/j02.eie.28977.
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This paper addresses the development of an acoustic deterrent device for the protection of fishponds and other objects against the unwanted presence of birds. The objective of the paper is not only providing of a deep analysis of available technologies for waveform synthesis and generation, but also building a theoretical base for the design and implementation of acoustic bird deterrent solutions. The paper addresses the synthesis of bird songs and calls using technologies for music, speech, and other types of acoustic signal processing. The second part of the paper is devoted to the unique algorithms and implementation details of the intelligent acoustic deterrence device prototype. The practical applicability of algorithms for bird call record conversion into synthesizer sequences has been analysed and possible issues are highlighted. The effectiveness and ease of practical implementation of the given method in the hardware are briefly discussed.
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Xia, Yuhao, Shilong Xu, Jiajie Fang, Ahui Hou, Youlong Chen, Xinyuan Zhang, and Yihua Hu. "A Novel Waveform Decomposition and Spectral Extraction Method for 101-Channel Hyperspectral LiDAR." Remote Sensing 14, no. 21 (October 22, 2022): 5285. http://dx.doi.org/10.3390/rs14215285.
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The 101-channel full-waveform hyperspectral LiDAR (FWHSL) is able to simultaneously obtain geometric and spectral information of the target, and it is widely applied in 3D point cloud terrain generation and classification, vegetation detection, automatic driving, and other fields. Currently, most waveform data processing methods are mainly aimed at single or several wavelengths. Hidden components are revealed mainly through optimization algorithms and comparisons of neighbor distance in different wavelengths. The same target may be misjudged as different ones when dealing with 101 channels. However, using the gain decomposition method with dozens of wavelengths will change the spectral intensity and affect the classification. In this paper, for hundred-channel FWHSL data, we propose a method that can detect and re-decompose the channels with outliers by checking neighbor distances and selecting specific wavelengths to compose a characteristic spectrum by performing PCA and clustering on the decomposition results for object identification. The experimental results show that compared with the conventional single channel waveform decomposition method, the average accuracy is increased by 20.1%, the average relative error of adjacent target distance is reduced from 0.1253 to 0.0037, and the degree of distance dispersion is reduced by 95.36%. The extracted spectrum can effectively characterize and distinguish the target and contains commonly used wavelengths that make up the vegetation index (e.g., 670 nm, 784 nm, etc.).
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Silva-Castro, Jhon. "A different methodology to control and predict ground vibrations from mine blasting." Canadian Geotechnical Journal 56, no. 7 (July 2019): 929–41. http://dx.doi.org/10.1139/cgj-2018-0073.
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Ground vibration prediction and control from mine blasting is a complex task involving disciplines such as geotechnical engineering, explosives engineering, and geology. Despite the importance of controlling ground vibrations, the mining industry commonly uses empirical methodologies based on concepts related to the nuclear tests era (charge weight scaling relationships). Over the past decade, attempts have been made to popularize more elaborate methodologies. These methods range from basic waveform superposition to analytical and numerical methods. Such methodologies are difficult to utilize daily because of their complexity, sometimes low accuracy (despite their sophistication), requirement for difficult to obtain parameters, and time needed to reach solutions using complex algorithms. In this paper, a different methodology to control and predict ground vibrations from blasting is presented. This methodology uses a semi-empirical approach, where the generation and propagation of ground vibration waves and the ground vibration characteristics at a point of interest are considered through the collection of a signature waveform. The methodology allows assessment of the optimum delay between charges to minimize and control ground vibration levels. A case study is included to demonstrate the benefits and the methodology in detail.
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Izzaqi, Fahmi Ahyar, Novie Ayub Windarko, and Ony Asrarul Qudsi. "Minimization of total harmonic distortion in neutral point clamped multilevel inverter using grey wolf optimizer." International Journal of Power Electronics and Drive Systems (IJPEDS) 13, no. 3 (September 1, 2022): 1486. http://dx.doi.org/10.11591/ijpeds.v13.i3.pp1486-1497.
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<span lang="EN-US">The inverter has been attracting researchers for their application in renewable energy. So far, multilevel inverter is considered as low distortion class, which produces multilevel output voltage imitating a pure sine waveform. However, the needs for free distortion of output voltage have been motivating to improve multilevel pulse width modulation PWM generation method. In this paper, the modified PWM technique is proposed to reduce the voltage total harmonics distortion (THD) of multilevel inverter. This modulation technique is then applied to control a single-phase threelevel neutral point clamped multilevel inverter (NPC-MLI). Grey wolf optimizer (GWO) algorithm is utilized to generate optimal amplitude and phase shift of modified reference signal. The GWO algorithm is then compared with other optimization algorithms such as differential evolution (DE), human psychology optimization (HPO), and particle swarm optimization (PSO) to evaluate their performance in harmonic minimization. The performance of the proposed work is validated through simulation and experimentation on a prototype. The results show that the modified PWM technique optimized with GWO can reduce THD on NPC-MLI output voltage.</span>
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Yahya, Ashraf, Syed M. Usman Ali, and Muhammad Farhan Khan. "Application of new algorithms on asymmetric cascaded multilevel inverter." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 39, no. 4 (July 24, 2020): 943–58. http://dx.doi.org/10.1108/compel-02-2020-0082.
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Purpose Multilevel inverter (MLI) is an established design approach for inverter applications in medium-voltage and high-voltage range of applications. An asymmetric design synthesizes multiple DC input voltage sources of unequal magnitudes to generate a high-quality staircase sinewave comprising a large number of steps or levels. However, the implications of using sources of unequal magnitudes results in the requirements of a large variety of inverter switches and higher magnitudes of the total blocking voltage (TBV) rating of the inverter, which increase the cost. The purpose of this study is to present a solution based on algorithms for establishing DC source magnitudes and other design parameters. Design/methodology/approach The approach used in this study is to develop algorithms that bring an asymmetric cascaded MLI (ACMLI) design close to symmetric design. This approach then reduces the variety of switch ratings and minimizes the TBV of the inverter. Thus, the benefits of both asymmetric design (generation of a large number of voltage levels in the output waveform) and symmetric design (modularity) are achieved. The proposed algorithms can be applied to a number of ACMLI topologies, including classical cascaded H-bridge (CHB). The effectiveness of the proposed algorithms is validated by simulation in Matlab-Simulink and experimental setup. Findings Two new algorithms are proposed that reduce the number of variety of switches to just three. The variety can further be reduced to two under a specified condition. The algorithms are compared with the existing ones, and the results are promising in minimizing the TBV rating of the inverter, which results in cost reduction as well. For a specific case of four CHBs, the proposed Algorithm-1 produced 27% and Algorithm-2 produced 53% higher levels. Moreover, the presented algorithms produced minimum values of the TBV and resulted in minimum cost of inverter. Originality/value The proposed algorithms are novel in structure and have achieved the targeted values of minimized switch variety and reduced TBV ratings. Due to less variety, the inverter achieves a near symmetric design, which enables to attain the added advantages of modularity and reduced difference of power sharing among the DC sources.
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Prisukhina, Ilona, Dmitry Borisenko, and Sergey Lunev. "Simulation Model of Electric Code-Modulated Signal in Russian Systems of Interval Control of Train Movement Based on Track Circuit." SPIIRAS Proceedings 18, no. 5 (September 19, 2019): 1212–38. http://dx.doi.org/10.15622/sp.2019.18.5.1212-1238.
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Systems of interval control of train movement Signaling systems, which are currently in service in Russian railways, use the electric track circuit as the main data channel between signals and locomotives. Code-modulated electric signals transferred through that channel are frequently get corrupted which leads to railway traffic delays.
Decoding of the electric signal received from a track circuit can be represented as an image classification problem, and thus the stability of the data channel could be significantly improved.
However, to build such a classifier based on some machine learning algorithm, one needs a large dataset. In this article, a simulation model to synthesize this dataset is proposed.
The structure of the computer model matches the main stages of the electric code-modulated signal generation in a track circuit: code signal generator, rails, locomotive receiver.
Based on code signal generator schematic and waveform diagrams, a generator algorithm is developed. At this stage, we modeled timings of electric code signals according to the specification as well as their random deviations caused by various factors.
The analysis of substitution circuits of the rail line revealed that it has the properties of a low-pass filter. So, the rail line using the Butterworth digital filter with corresponding parameters is modeled. Additionally, at this stage, random noise during transmission was taken into account.
A similar technique is applied for modeling of a locomotive receiver which has a band-pass filter as the first signal processing block.
Thus, the proposed simulation model consists of a set of algorithms which run in series. By varying the parameters of the model, one can synthesize waveform diagrams of the electric code-modulated signal received by the locomotive equipment from a track circuit working in various modes and conditions.
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Berkhout, A. J. "Combining full wavefield migration and full waveform inversion, a glance into the future of seismic imaging." GEOPHYSICS 77, no. 2 (March 2012): S43—S50. http://dx.doi.org/10.1190/geo2011-0148.1.
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The next generation seismic migration and inversion technology considers multiple scattering as vital information, allowing the industry to derive significantly better reservoir models — with more detail and less uncertainty—while requiring a minimum of user intervention. Three new insights have been uncovered with respect to this fundamental transition. Unblended or blended multiple scattering can be included in the seismic migration process, and it has been proposed to formulate the imaging principle as a minimization problem. The resulting process yields angle-dependent reflectivity and is referred to as recursive full wavefield migration (WFM). The full waveform inversion process for velocity estimation can be extended to a recursive, optionally blended, anisotropic multiple-scattering algorithm. The resulting process yields angle-dependent velocity and is referred to as recursive full waveform inversion (WFI). The mathematical equations of WFM and WFI have an identical structure, but the physical meaning behind the expressions is fundamentally different. In WFM the reflection process is central, and the aim is to estimate reflection operators of the subsurface, using the up- and downgoing incident wavefields (including the codas) in each gridpoint. In WFI, however, the propagation process is central and the aim is to estimate velocity operators of the subsurface, using the total incident wavefield (sum of up- and downgoing) in each gridpoint. Angle-dependent reflectivity in WFM corresponds with angle-dependent velocity (anisotropy) in WFI. The algorithms of WFM and WFI could be joined into one automated joint migration-inversion process. In the resulting hybrid algorithm, being referred to as recursive joint migration inversion (JMI), the elaborate volume integral solution was replaced by an efficient alternative: WFM and WFI are alternately applied at each depth level, where WFM extrapolates the incident wavefields and WFI updates the velocities without any user interaction. The output of the JMI process offers an integrated picture of the subsurface in terms of angle-dependent reflectivity as well as anisotropic velocity. This two-fold output, reflectivity image and velocity model, offers new opportunities to extract accurate rock and pore properties at a fine reservoir scale.
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Jasvir Singh, Puneet Chopra and Simerpreet Singh. "A Research Study and Comparative Analysis of MPPT Controllers for PV Cells with Algorithmatic Structures." International Journal for Modern Trends in Science and Technology 7, no. 07 (February 20, 2022): 72–76. http://dx.doi.org/10.46501/ijmtst051235.
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Due to continues increase in usage of various sources of Energies, Solar energy becomes very popular source of renewable energy due to its several advantages. Systems such as Photovoltaic (PV) power systems have been widely used in many applications of generation and utilization of energy in many countries. But also, there are many urgent problems to cop up with the applications of PV Cells for the purpose of Power Generation and in the power systems such as low efficiency, high cost etc. The main Concentration is to how to improve efficiency. Since generally Photovoltaic (PV) arrays exhibit a nonlinear power–voltage (P–V) characteristic curve which have a variation with isolation and temperature. To achieve good efficiency, Maximum Power Point Tracking (MPPT) is a very important technology. There are various conventional MPPT schemes have been proposed and working on including Hill-Climbing (HC) , Perturb and Observe (P&O) , and Incremental Conductance (INC) etc. In this research work, the optimization methods for efficient tracking such as PSO and GSA are explored. The very essential and considered issue of this type of control (MPPT) is to how to achieve the best optimized status and this can be achieved by using evolutionary algorithms. PSO algorithm owns the characteristics methods like parallel processing, good robustness, and high probability of finding global optimal solution. By adding GSA with PSO ,it can be improved. Advantage of adding proposed GSAPSO algorithm greatly shortens the searching time, helpful in reducing the fluctuation of output waveform and thus improves the optimization and efficiency through particles dormancy and activation control, optimal number of particles algorithm and search sequence selection. It achieves a smooth starting for maximum power and achieves it in less time than the widely used other methods.
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Jing, Tao, Alexander Maklakov, Andrey Radionov, Vadim Gasiyarov, and Yuehua Liang. "Formulations, Solving Algorithms, Existing Problems and Future Challenges of Pre-Programmed PWM Techniques for High-Power AFE Converters: A Comprehensive Review." Energies 15, no. 5 (February 24, 2022): 1696. http://dx.doi.org/10.3390/en15051696.
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Considering the development of the hybrid wind and solar photovoltaic generation and smart grid, Active Front-End (AFE) converters for high-power applications are facing significant opportunities and challenges related to power quality and efficiency. The Pre-programmed Pulse-Width Modulation (PPWM) techniques can strictly control the harmonic spectrum of a specified voltage or current waveform generated by a high-power AFE converters, and have been extensively applied to reduce or even eliminate the harmonic distortion with low switching losses for high-power converters in order to deal with these issues aforementioned. For the PPWM techniques with low switching frequency, Selective Harmonic Elimination (SHE) and Selective Harmonic Mitigation (SHM) have been the prevailing solutions and gain widespread popularity, among which SHM can provide further control of the harmonic spectrum in cases of similar switching losses to SHE. Over the past several decades, the applications of SHE and SHM have been extended to high-power AFE converters. Thus, the aim of this study is to provide a comprehensive literature review regarding their various formulations, solving algorithms, and existing problems to high-power AFE converters. In addition, the suggestions for future applications of PPWM in high-power AFE converters are also discussed.
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Lam, Frank, Hsiang-Wei Lu, Chung-Che Wu, Zekeriya Aliyazicioglu, and James S. Kang. "Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction." Computational and Mathematical Methods in Medicine 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/6975085.
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Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C, peripheral resistance R, aortic impedance r, and the inertia of blood L, to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies.
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Hong, Sheng, Yantao Dong, Rui Xie, Yu Ai, and Yuhao Wang. "Constrained Transmit Beampattern Design Using a Correlated LFM-PC Waveform Set in MIMO Radar." Sensors 20, no. 3 (January 31, 2020): 773. http://dx.doi.org/10.3390/s20030773.
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This paper considers the design of a desired transmit beampattern under the good ambiguity function constraint using a correlated linear frequency modulation-phase coded (LFM-PC) waveform set in multiple-input-multiple-output (MIMO) radar. Different from most existing beampattern design approaches, we propose using the LFM-PC waveform set to conquer the challenging problem of synthesizing waveforms with constant-envelope and easy-generation properties, and, meanwhile, solve the hard constraint of a good ambiguity behaviour. First, the ambiguity function of the LFM-PC waveform set is derived, and the superiority of LFM-PC waveforms over LFM and PC waveforms is verified. The temporal and spatial characteristic analysis of the LFM-PC waveform set demonstrates that both the transmit beampattern and sidelobe level are mainly affected by the frequency intervals, bandwidths, and phase-coded sequences of the LFM-PC waveform set. Finally, the constrained beampattern design problem is formulated by optimizing these parameters for desired beampatterns and low sidelobes at different doppler frequencies, which is a bi-objective optimization problem. To solve this, we propose a joint optimization strategy followed by a mandatory optimization, where the sequence quadratic programming (SQP) algorithm and adaptive clonal selection (ACS) algorithm are exploited iteratively. The simulation results demonstrate the efficiency of our proposed method.
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SWAPNA, GOUTHAM, DHANJOO N. GHISTA, ROSHAN JOY MARTIS, ALVIN P. C. ANG, and SUBBHURAAM VINITHA SREE. "ECG SIGNAL GENERATION AND HEART RATE VARIABILITY SIGNAL EXTRACTION: SIGNAL PROCESSING, FEATURES DETECTION, AND THEIR CORRELATION WITH CARDIAC DISEASES." Journal of Mechanics in Medicine and Biology 12, no. 04 (September 2012): 1240012. http://dx.doi.org/10.1142/s021951941240012x.
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The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.
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Zhang, Hua Long, and Er Hong Zhang. "Study on Automatic Control Method of Photovoltaic Power Systems." Advanced Materials Research 1070-1072 (December 2014): 52–55. http://dx.doi.org/10.4028/www.scientific.net/amr.1070-1072.52.
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Maximum power point tracking (MPPT) algorithm directly affect the conversion efficiency of photovoltaic power generation system independent of energy, the thesis of common strengths and weaknesses of the MPPT algorithm to summarize, the traditional method of perturbation and observation maximum power point tracking algorithm is improved and build a simulation model, the simulation model are direct and network controllers and input grid and grid-independent photovoltaic power generation system model of network simulation, and network time from both the grid conditions, and the impact of current network waveforms and grid voltage waveform analyzed. Simulation results observed with the conventional perturbation method were compared and analyzed to verify the effectiveness of the improved method.
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Song, Wongeun, Se Young Jung, Hyunyoung Baek, Chang Won Choi, Young Hwa Jung, and Sooyoung Yoo. "A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study." JMIR Medical Informatics 8, no. 7 (July 31, 2020): e15965. http://dx.doi.org/10.2196/15965.
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Background Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. Objective The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. Methods We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. Results The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. Conclusions The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.
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Tuethong, Piyapon, Krit Kitwattana, Peerawut Yutthagowith, and Anantawat Kunakorn. "An Algorithm for Circuit Parameter Identification in Lightning Impulse Voltage Generation for Low-Inductance Loads." Energies 13, no. 15 (July 31, 2020): 3913. http://dx.doi.org/10.3390/en13153913.
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This paper presents an effective technique based on an artificial neural network algorithm utilized for circuit parameter identification in lightning impulse generation for low inductance loads such as low voltage windings of a power transformer, a large distribution transformer and an air core reactor. The limitation of the combination between Glaninger’s circuit and the circuit parameter selection from Feser’s suggestions in term of producing an impulse waveform to be compliant with standard requirements when working with a low inductance load is discussed. In Feser’s approach, the circuit parameters of the generation circuit need to be further adjusted to obtain the waveform compliant with the standard requirement. In this process, trial and error approaches based on test engineers’ experience are employed in the circuit parameter selection. To avoid the unintentional damage from electrical field stress during the voltage waveform adjustment process, circuit simulators, such as Pspice and EMTP/ATP, are very useful to examine the generated voltage waveform before the experiments on the test object are carried out. In this paper, a system parameter identification based on an artificial neural network algorithm is applied to determine the appropriate circuit parameters in the test circuit. This impulse voltage generation with the selected circuit parameters was verified by simulations and an experiment. It was found that the generation circuit gives satisfactory impulse voltage waveforms in accordance with the standard requirement for the maximum charging capacitance of 10 µF and the load inductance from 400 µH to 4 mH. From the simulation and experimental results of all cases, the approach proposed in this paper is useful for test engineers in selection of appropriate circuit components for impulse voltage tests with low inductance loads instead of employing conventional trial and error in circuit component selection.
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Colombo, Daniele, Diego Rovetta, and Ersan Turkoglu. "CSEM-regularized seismic velocity inversion: A multiscale, hierarchical workflow for subsalt imaging." GEOPHYSICS 83, no. 5 (September 1, 2018): B241—B252. http://dx.doi.org/10.1190/geo2017-0454.1.
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Seismic imaging in salt geology is complicated by highly contrasted velocity fields and irregular salt geometries, which cause complex seismic wavefield scattering. Although the imaging challenges can be addressed by advanced imaging algorithms, a fundamental problem remains in the determination of robust velocity fields in high-noise conditions. Conventional migration velocity analysis is often ineffective, and even the most advanced methods for depth-domain velocity analysis, such as full-waveform inversion, require starting from a good initial estimate of the velocity model to converge to a correct result. Nonseismic methods, such as electromagnetics, can help guide the generation of robust velocity models to be used for further processing. Using the multiphysics data acquired in the deepwater section of the Red Sea, we apply a controlled-source electromagnetic (CSEM) resistivity-regularized seismic velocity inversion for enhancing the velocity model in a complex area dominated by nappe-style salt tectonics. The integration is achieved by a rigorous approach of multiscaled inversions looping over model dimensions (1D first, followed by 3D), variable offsets and increasing frequencies, data-driven and interpretation-supported approaches, leading to a hierarchical inversion guided by a parameter sensitivity analysis. The final step of the integration consists of the inversion of seismic traveltimes subject to CSEM model constraints in which a common-structure coupling mechanism is used. Minimization is performed over the seismic data residuals and cross-gradient objective functions without inverting for the resistivity model, which is used as a reference for the seismic inversion (hierarchical approach). Results are demonstrated through depth imaging in which the velocity model derived through CSEM-regularized hierarchical inversion outperforms the results of a seismic-only derived velocity model.
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Bhadoria, Prakhar, and Divyanshi Yadav. "Differential Evolutionary and PI Regulated Converter Technology for Loading Analysis in Renewable Energy Systems." SMART MOVES JOURNAL IJOSCIENCE 6, no. 11 (November 24, 2020): 1–8. http://dx.doi.org/10.24113/ijoscience.v6i11.322.
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Many remote communities around the world cannot be physically or economically connected to an electrical grid. The main objective of the study of designing an inverter control that attains a lower distortion level in the voltage as well as current waveforms by incorporating an optimization algorithm. The controller should reduce the spikes at the transient loading point when the system is subjected to sudden load changes at the power generating units. And the system is to be integrated with the fuel system also to obtain energy efficiency. The fuel system would be connected in parallel to the DC voltage output of the solar/wind hybrid system. The final hybrid system with fuel cell integration was studied for the total harmonic distortion in the voltage and current waveform. The distortion level in the voltage waveform was found to be 0.25% and that in the current waveform was 1.84%. It is under the IEEE acceptable limits.
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Yutthagowith, Peerawut, Sutee Leejongpermpoon, and Nawakun Triruttanapiruk. "A Simplified Model of a Surge Arrester and Its Application in Residual Voltage Tests." Energies 14, no. 11 (May 27, 2021): 3132. http://dx.doi.org/10.3390/en14113132.
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A simplified and accurate model of a surge arrester used in the residual voltage test is proposed in this paper. With the help of a genetic algorithm, the measured impulse current and residual voltage waveforms are utilized to determine circuit parameters of the proposed model and the generation circuit precisely. The technique starts from the circuit parameter determination using the preliminary experimental data with a lower current peak than that specified by the standard. From the determined model and with the help of the genetic algorithm, the circuit parameters and the charging voltage to obtain the specified current peak and the residual voltage can be estimated accurately. The validity of the proposed technique has been verified by experiments for the estimation of the appropriate current circuit parameters, the charging voltage, and the residual voltage. In addition, the application of the proposed model in the residual voltage tests is presented. From comparison of simulated and experimental results with the determined parameters, the impulse current and residual voltage waveforms are determined precisely. It is confirmed that the proposed model and technique are attractive in the appropriate circuit parameter determination and the residual voltage estimation in the residual voltage tests of surge arresters. The proposed method also provides a good advantage for reduction of the number of trial and error experiments for obtaining the current waveform according to the standard requirement. Moreover, the unintentional damages of the arrester during the process of the waveform adjustment will be reduced significantly.
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Xi, Jinxiang, Mohamed Talaat, Xiuhua Si, and Haibo Dong. "Flow Dynamics and Acoustics from Glottal Vibrations at Different Frequencies." Acoustics 4, no. 4 (October 28, 2022): 915–33. http://dx.doi.org/10.3390/acoustics4040056.
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Glottal vibration is fundamental to breathing-related disorders and respiratory sound generation. However, responses of the flow and acoustics to glottal vibrations of different frequencies are unclear. The objective of this study is to numerically evaluate the influences of glottal vibration frequencies on inspiratory airflow dynamics and flow-induced sound signals; this is different from normal phonation that is driven by controlled expiratory flows. A computational model was developed that comprised an image-based mouth–throat–lung model and a dynamic glottis expanding/contracting following a sinusoidal waveform. Large Eddy simulations were used to solve the temporal and spatial flow evolutions, and pressure signals were analyzed using different transform algorithms (wavelet, Hilbert, Fourier, etc.). Results show that glottal vibrations significantly altered the flows in the glottis and trachea, especially at high frequencies. With increasing vibration frequencies, the vortices decreased in scale and moved from the main flow to the walls. Phase shifts occurred between the glottis motion and glottal flow rates for all frequencies considered. Due to this phase shift, the pressure forces resisted the glottal motion in the first half of contraction/expansion and assisted the glottal motion in the second half of contraction/expansion. The magnitude of the glottal flow fluctuation was approximately linear with the vibration frequency (~f0), while the normal pressure force increased nonlinearly with the frequency (~f01.85). Instantaneous pressure signals were irregular at low vibration frequencies (10 and 20 Hz) but became more regular with increasing frequencies in the pressure profile, periodicity, and wavelet-transformed parameters. The acoustic characteristics specific to the glottal vibration frequency were explored in temporal and frequency domains, which may be used individually or as a combination in diagnosing vocal fold dysfunction, snoring, sleep apnea, or other breathing-related diseases.
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Song, Chengyun, Lin Li, Lingxuan Li, and Kunhong Li. "Robust K-means algorithm with weighted window for seismic facies analysis." Journal of Geophysics and Engineering 18, no. 5 (August 20, 2021): 618–26. http://dx.doi.org/10.1093/jge/gxab039.
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Abstract Seismic facies analysis can generate a map to describe the spatial distribution characteristics of reservoirs, and therefore plays a critical role in seismic interpretation. To analyse the characteristics of the horizon of interest, it is usually necessary to extract seismic waveforms along the target horizon using a selected time window. The inaccuracy of horizon interpretation often produces some inconsistent phases and leads to inaccurate classification. Therefore, the developed adaptive phase K-means algorithm proposed a sliding time window to extract seismic waveforms. However, setting the maximum offset of the sliding window is difficult in a real data application. A value that is too large may cause the cross-layer problem, whereas a value that is too small reduces the flexibility of the algorithm. To address this disadvantage, this paper proposes a robust K-means (R-K-means) algorithm with a Gaussian-weighted sliding window for seismic waveform classification. The used weights punish those windows distant from the interpretation horizon in the objective function, consequently producing a smaller range of horizon adjustments even when using relatively large maximum offsets and benefitting the generation of stable and reliable seismic facies maps. The application of real seismic data from the F3 block proves the effectiveness of the proposed algorithm.
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Pralon, Leandro, Gabriel Beltrao, Alisson Barreto, and Bruno Cosenza. "On the Analysis of PM/FM Noise Radar Waveforms Considering Modulating Signals with Varied Stochastic Properties." Sensors 21, no. 5 (March 3, 2021): 1727. http://dx.doi.org/10.3390/s21051727.
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Noise Radar technology is the general term used to describe radar systems that employ realizations of a given stochastic process as transmit waveforms. Originally, carriers modulated in amplitude by a Gaussian random signal, derived from a hardware noise source, were taken into consideration, justifying the adopted nomenclature. With the advances made in hardware as well as the rise of the software defined noise radar concept, waveform design emerges as an important research area related to such systems. The possibility of generating signals with varied stochastic properties increased the potential in achieving systems with enhanced performances. The characterization of random phase and frequency modulated waveforms (more suitable for several applications) has then gained considerable notoriety within the radar community as well. Several optimization algorithms have been proposed in order to conveniently shape both the autocorrelation function of the random samples that comprise the transmit signal, as well as their power spectrum density. Nevertheless, little attention has been driven to properly characterize the stochastic properties of those signals through closed form expressions, jeopardizing the effectiveness of the aforementioned algorithms as well as their reproducibility. Within this context, this paper investigates the performance of several random phase and frequency modulated waveforms, varying the stochastic properties of their modulating signals.
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Rasheed, Mohammed, Moataz M. A. Alakkad, Rosli Omar, Marizan Sulaiman, and Wahidah Abd Halim. "Enhance the accuracy of control algorithm for multilevel inverter based on artificial neural network." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1148. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1148-1158.
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<p>In converters or multilevel inverters it is very important to ensure that the output of the<br />multilevel inverters waveforms in term of the voltage or current of the waveforms is<br />smooth and without distortion. The artificial neural network (ANN) technique to<br />obtaining proper switching angles sequences for a uniform step asymmetrical modified<br />multilevel inverter by eliminating specified higher-order harmonics while maintaining<br />the required fundamental voltage and current waveform. However, through this paper a<br />modified CHB-MLI are proposed using artificial intelligence optimization technique<br />based on modulation Selective Harmonic Elimination (SHE-PWM). A most powerful<br />modulation technique that used to minimize a harmonic contants during the outout<br />waveform of multilevel inverter is a SHE-PWM method. The proposed a five-level<br />Modified Cascaded H-Bridge Multilevel Inverter (M-CHBMI) with ANN controller to<br />improve the output voltage and current performance and achieve a lower Total<br />Harmonic Distortion (THD). The main aims of this paper cover design, modeling,<br />prediction for real-time generation of optimal switching angles in a single-phase<br />topology of modified five level CHB-MLI. due to the heavy cost of computation to<br />solving transcendental nonlinear equations with specified number, a real-time<br />application of Selective Harmonic Elimination-Pulse Width Modulation (SHE-PWM)<br />technique is limited. SHE equations known as a transcendental nonlinear equation that<br />contain trigonometric functions. The prototype of a 5-level inverter in Digital Signal<br />Processing (DSP) TMS320F2812 reveals that the proposed method is highly efficient<br />for harmonic reduction in modified multilevel inverter.</p>
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Dimitrov, T. S., M. He, and M. J. Prerau. "0449 Characterizing Spindle Activity as a Time-Frequency Phenomenon." Sleep 43, Supplement_1 (April 2020): A172. http://dx.doi.org/10.1093/sleep/zsaa056.446.
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Abstract Introduction Spindles are currently defined clinically based on observed patterns in the EEG waveform trace, with automated methods seeking to replicate visual scoring by experts. Recent work suggests that sleep spindles may be more readily observed as time-frequency peaks in the EEG spectrogram. This study compares spectral peaks in the multitaper spectrogram to expert and automatic detection scoring, characterizes the variability of spindles across a night, and investigates topographical and temporal clustering of spindles within individual EEG records. Methods We compared spectral peaks, expert scoring, and automatic detection in two datasets (DREAMS, and a high-density control study). Peaks were identified using multitaper spectral estimation and the peak prominence of the normalized power spectrum for each channel. Spatiotemporal variability analysis was performed using cluster and pattern recognition algorithms including penalized sorting of channel activation order, 2D-cross correlation, PCA and UMAP cluster analysis, and the seqNMF method. Results Spectral peaks were shown to be highly robust to and easily differentiated from broadband noise, occuring at rates (10-16 per min) far exceeding spindle rates reported in literature (~2.5 per min). Expert scoring and automated scoring failed to capture clear spectral peaks in the time-frequency domain, indicating an underreporting of the phenomenology. No apparent clustering or patterns of sleep spindle-like activity was observed using the proposed methods, suggesting high variability of spatiotemporal evolution of spindles. Conclusion These results suggest that the difficulty of time-domain visual scoring of spindles causes an artificially low estimate of the underlying phenomenology, which is mirrored in the assumptions implicit in the thresholds of automated scorers. This work shows that spindles are highly variable in their spatiotemporal evolution, suggesting that there is no optimal single electrode for analysis and casting doubt on the presence of a single cortical generation mechanism. We must therefore revisit the concept of the spindle using the time-frequency domain to more robustly characterize underlying phenomenology. Support National Institute Of Neurological Disorders And Stroke Grant R01 NS-096177
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Majka, Ł., and S. Paszek. "Mathematical model parameter estimation of a generating unit operating in the Polish National Power System." Bulletin of the Polish Academy of Sciences Technical Sciences 64, no. 2 (June 1, 2016): 409–16. http://dx.doi.org/10.1515/bpasts-2016-0046.
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Abstract In the paper, a method and results of parameter estimation of the mathematical model of a generating unit operating in the Polish National Power System are presented. Computations of the parameters were carried out based on measurement and simulation of dynamic waveforms of selected quantities of the generating unit. The problem of parameter identification was brought to minimization of the objective function determined by the vector of deviations between the approximated and approximating waveforms computed on the basis of the models expressed by the searched parameters. A hybrid optimization algorithm, being a serial combination of genetic and gradient algorithms, was used for minimization of the objective function. A methodology for filtering the recorded measurement signals is proposed in the paper. Method and results calculation of the sensitivity to changes of the model parameters of selected dynamic waveforms are also presented.
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Pruski, P., and S. Paszek. "Assessment of Polish Power System angular stability based on analysis of different disturbance waveforms." Bulletin of the Polish Academy of Sciences Technical Sciences 63, no. 2 (June 1, 2015): 435–41. http://dx.doi.org/10.1515/bpasts-2015-0049.
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Abstract The paper presents investigation results concerning assessment of the Polish Power System (PPS) angular stability based on power system state matrix eigenvalues associated with electromechanical phenomena, when using the angular stability factors calculated on the basis of these eigenvalues. The eigenvalues were calculated by analysis of the disturbance waveforms of the instantaneous power, angular speed and power angle of synchronous generators in PPS generating units when taking into account introduction of a disturbance to different units. There was assumed a disturbance in the form of a rectangular pulse introduced to the voltage regulation system of a generating unit. There was also analysed the effect of the duration of the introduced test disturbance on the calculation results of the eigenvalue influencing the measurement waveforms of the instantaneous power of generating unit no 5 in Rybnik Power Plant. The method for eigenvalue calculations used in the investigations consists in approximation of the disturbance waveforms in particular generating units with the use of the waveforms being a superposition of the modal components associated with the searched eigenvalues. The hybrid optimisation algorithm being a serial combination of the genetic and gradient algorithms was used for computations
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Zhen, Liying, Yan Zhao, Pin Zhang, Congwei Liao, Xiaohui Gao, and Lianwen Deng. "Implementation of Adaptive Real-Time Camouflage System in Visible-Light Band." Applied Sciences 11, no. 15 (July 21, 2021): 6706. http://dx.doi.org/10.3390/app11156706.
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This paper presents an adaptive camouflage system in visible band, featuring a dominant color feature-matching algorithm and pulse width modulation (PWM)-based display driving circuit. The control system consists of three parts, namely, the background sensing part, the central processing part, and the physical driving waveform generation part. Images of the local environment are sampled by the background sensing part, and then the dominant color feature matching algorithm is conducted to select a proper camouflage image that matches the local environment. Consequently, the cholesteric liquid crystals (CLCs) display using amplitude adjustable AC voltage, which is modulated by the physical driving waveform generation unit. The experimental results show that the matching degree of the proposed algorithm was 2.47 times that of the conventional hue (H), saturation (S), and value (V) histogram camouflage evaluation method, while the output peak wavelength of the reflective band can be adjusted from 604 to 544 nm according to the ambient color profile.
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Gorokhovskyi, Semen, and Artem Laiko. "Euclidean Algorithm for Sound Generation." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 48–51. http://dx.doi.org/10.18523/2617-3808.2021.4.48-51.
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Euclidean algorithm is known by humanity for more than two thousand years. During this period many applications for it were found, covering different disciplines and music is one of those. Such algorithm application in music first appeared in 2005 when researchers found a correlation between world music rhythm and the Euclidean algorithm result, defining Euclidean rhythms as the concept.In the modern world, music could be created using many approaches. The first one being the simple analogue, the analogue signal is just a sound wave that emitted due to vibration of a certain medium, the one that is being recorded onto a computer hard drive or other digital storage called digital and has methods of digital signal processing applied. Having the ability to convert the analogue signal or create and modulate digital sounds creates a lot of possibilities for sound design and production, where sonic characteristics were never accessible because of limitations in sound development by the analogue devices or instruments, nowadays become true. Sound generation process, which usually consists of modulating waveform and frequency and can be influenced by many factors like oscillation, FX pipeline and so on. The programs that influence synthesised or recorded signal called VST plugins and they are utilising the concepts of digital signal processing.This paper aims to research the possible application of Euclidean rhythms and integrate those in the sound generation process by creating a VST plugin that oscillates incoming signal with one of the four basic wave shapes in order to achieve unique sonic qualities. The varying function allows modulation with one out of four basic wave shapes such as sine, triangle, square and sawtooth, depending on the value received from the Euclidean rhythm generator, switching modulating functions introduces subharmonics, with the resulting richer and tighter sound which could be seen on the spectrograms provided in the publication.
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Peng, Ju, Jin Huang, Jianjun Wang, Fanbo Meng, Hongxiao Gong, and Bu Ping. "The Driving Waveform Design Method of Power-Law Fluid Piezoelectric Printing Based on Iterative Learning Control." Sensors 22, no. 3 (January 25, 2022): 935. http://dx.doi.org/10.3390/s22030935.
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In some applications of piezoelectric three-dimensional inkjet printing, the materials used are power-law fluids as they are shear thinning. Their time-varying viscosities affect the droplet formation, which is determined by the volume flow rate at the nozzle outlet. To obtain a fine printing effect, it is necessary to present a driving waveform design method that considers the shear-thinning viscosities of materials to control the volume flow rate at the nozzle outlet, which lays the foundation for the single and stable droplet generation during the printing process. In this research, we established the relationship between the driving waveform and the volume flow rate at the nozzle outlet by modifying a model that describes the inkjet mechanism of power-law fluid. The modified model was used to present a driving waveform design method based on iterative learning control. The iterative learning law of the method was designed based on the gradient descent algorithm and demonstrated its convergence. The driving waveform design method was verified to be practical and feasible by implementing drop generation experiments.
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Zhao, Yujie, Anna Kuwana, Yuan Yang Du, Yuki Ozawa, Yuto Sasaki, Haruo Kobayashi, Takayuki Nakatani, et al. "Consideration on Input Signal for ADC Histogram Test in Short Time." Advanced Engineering Forum 38 (November 2020): 83–92. http://dx.doi.org/10.4028/www.scientific.net/aef.38.83.
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This paper describes two algorithms for generating test signals to efficiently test the linearity of ADCs. Linearity is an important testing item for ADCs, and it takes a long time (hence is costly) to test especially low-sampling-rate, high-resolution ADCs. We have proposed to generate a test signal consisting of multiple-sine waves, to precisely test the linearity for specific important codes (such as around the center of the output codes), using an arbitrary waveform generator (AWG) and an analog filter in our previous studies. Here, we formulate them for general use. Their verification is performed on a single sine wave, and good results are obtained to show the validity of the derivation algorithm.
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Fu, Zhihe, Yibiao Fan, Xiaowei Cai, Zhaohong Zheng, Jiaxiang Xue, and Kun Zhang. "Lithium Titanate Battery Management System Based on MPPT and Four-Stage Charging Control for Photovoltaic Energy Storage." Applied Sciences 8, no. 12 (December 6, 2018): 2520. http://dx.doi.org/10.3390/app8122520.
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To overcome the unstable photovoltaic input and high randomness in the conventional three-stage battery charging method, this paper proposes a charging control strategy based on a combination of maximum power point tracking (MPPT), and an enhanced four-stage charging algorithm for a photovoltaic power generation energy storage system. This control algorithm ensures that the charging process is not affected by fluctuations in the photovoltaic power. The discharge bus waveform, push–pull discharge load switching waveform, push–pull circuit efficiency, and voltage and current regulation accuracies of the system were investigated. The experimental results show that the charging process is consistent with the designed four-stage charging control algorithm, the voltage and current regulation accuracies satisfy the charging requirements, the busbar remained stable during the battery charging and discharging switch, and the battery balancing effect was good.
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Huang, Rongxin, Zhigang Zhang, Zedong Wu, Zhiyuan Wei, Jiawei Mei, and Ping Wang. "Full-waveform inversion for full-wavefield imaging: Decades in the making." Leading Edge 40, no. 5 (May 2021): 324–34. http://dx.doi.org/10.1190/tle40050324.1.
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Seismic imaging using full-wavefield data that includes primary reflections, transmitted waves, and their multiples has been the holy grail for generations of geophysicists. To be able to use the full-wavefield data effectively requires a forward-modeling process to generate full-wavefield data, an inversion scheme to minimize the difference between modeled and recorded data, and, more importantly, an accurate velocity model to correctly propagate and collapse energy of different wave modes. All of these elements have been embedded in the framework of full-waveform inversion (FWI) since it was proposed three decades ago. However, for a long time, the application of FWI did not find its way into the domain of full-wavefield imaging, mostly owing to the lack of data sets with good constraints to ensure the convergence of inversion, the required compute power to handle large data sets and extend the inversion frequency to the bandwidth needed for imaging, and, most significantly, stable FWI algorithms that could work with different data types in different geologic settings. Recently, with the advancement of high-performance computing and progress in FWI algorithms at tackling issues such as cycle skipping and amplitude mismatch, FWI has found success using different data types in a variety of geologic settings, providing some of the most accurate velocity models for generating significantly improved migration images. Here, we take a step further to modify the FWI workflow to output the subsurface image or reflectivity directly, potentially eliminating the need to go through the time-consuming conventional seismic imaging process that involves preprocessing, velocity model building, and migration. Compared with a conventional migration image, the reflectivity image directly output from FWI often provides additional structural information with better illumination and higher signal-to-noise ratio naturally as a result of many iterations of least-squares fitting of the full-wavefield data.
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Bezha, Minella, Makoto Ishii, Takahiro Shoda, Yuki Hoshide, and Naoto Nagaoka. "Innovative Scaled Hardware Simulator for Designing and Testing an EV’s Battery Storage System Incorporated with an Adaptive ANN Model." Applied System Innovation 3, no. 2 (June 23, 2020): 27. http://dx.doi.org/10.3390/asi3020027.
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In this study, a scaled-down system, which can be used as a benchmark test for the battery storage designing of electric vehicles (EVs) is proposed. This model was based on the hardware simulator of the battery storage system (BSS) used from a single cell up to 4 cells in a series pack system, which simulates a practical battery pack. The developed simulator can charge and discharge any rechargeable battery, such as Li-Ion, Ni–MH or Pb battery. The scaling ratio of the simulator was evaluated by the ratio of the current or power of the battery pack specimen related to the specification. Also, this study proposes an innovative state of charge (SoC) estimation of the battery pack for EVs based on genuine results obtained through practical tests. This estimation was carried out by an adaptive artificial neural network (ANN) algorithm, using simple inputs. As well, this model can deduct the state of health (SoH) of the battery pack based on the power output level and waveform characteristics. The results of the ANN showed high generalization, a low error of SoC estimation at the level of 1.1%, with a calculation time less than 16.5 s. Regarding the hardware simulator, the similarity of the results and waveform accuracy of the scaled-down battery systems compared with the real battery pack was very acceptable with a maximum deviation of 2.1% in the worst scenario. The cells cycled with different depths of discharge (DoD) or C-rates, at different temperatures with different initial SoCs using any arbitrary current waveforms. Our conclusions will help battery manufacturers to test and evaluate the performance of the BSS in different applications, such as EVs, PV generation, and wind farm, with significant cost reduction. Also, the ANN algorithm can be used and embedded in EVs or in any other industrial application, as proposed in this paper. This study contributed to the real-time diagnosis of the BSS without interrupting the normal operation based on its features.
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Manousakis, Stelios. "Non-Standard Sound Synthesis with L-Systems." Leonardo Music Journal 19 (December 2009): 85–94. http://dx.doi.org/10.1162/lmj.2009.19.85.
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This paper presents a new non-standard technique for waveform synthesis in the time domain using L-systems, a formalism related to grammars, fractals and automata. This technique, developed as part of a larger-scale compositional system, is based on waveform segmentation and offers various methods for generating wavetables. The paper first introduces L-systems and some specifics of their interpretation and discusses extensions such as incorporating genetic algorithms and designing hierarchical L-systems and L-system networks. The second half describes the implementation model in detail, proposes some sound synthesis strategies and presents paths for further work.
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Amir Tokić and Ivo Uglešić. "THE NUMERICAL CALCULATION OF LOW FREQUENCY ELECTROMAGNETIC TRANSIENT PHENOMENA IN POWER TRANSFORMERS." Journal of Energy - Energija 56, no. 5 (November 22, 2022): 584–607. http://dx.doi.org/10.37798/2007565368.
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In this article, a transformer model is presented that is applicable to low frequency electromagnetic transient phenomena of up to 1 kHz. An example of the energization of a no-load transformer is analyzed. A simplifed analytical approach is presented first. Due to the limitations of this approach, a numerical approach is introduced for the solution of the stiff differential equations that describe the transient phenomena. In both cases, algorithms have been developed for generating the waveforms of the state variables. The results of both algorithms are compared to the results of the MATLAB/Simulink/Power System Blockset for the analysis of electromagnetic transient phenomena in electrical energy systems. Developed algorithm with the introduced numerical approach can be used successfully in other low frequency transient phenomena where the main subject of analysis is the nonlinear character of the transformer: ferroresonance, load switch-off, transformer faults etc.
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Zalasiński, Marcin, Krzysztof Cpałka, Łukasz Laskowski, Donald C. Wunsch, and Krzysztof Przybyszewski. "An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors." Journal of Artificial Intelligence and Soft Computing Research 10, no. 3 (July 1, 2020): 173–87. http://dx.doi.org/10.2478/jaiscr-2020-0012.
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AbstractIn biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates dispro-portions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.
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Dayana, R., and R. Kumar. "Modified isotropic orthogonal transform algorithm-universal filtered multicarrier transceiver for 5G cognitive radio application." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 3100. http://dx.doi.org/10.11591/ijece.v9i4.pp3100-3107.
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Rapid developments in modern wireless communication permit the trade of spectrum scarcity. Higher data rate and wider bandwidth emerge the development in growing demand of wireless communication system. The innovative solution for the spectrum scarcity is cognitive radio (CR). Cognitive radio is the significant technology used to utilize the spectrum effectively. The important aspect of CR is sensing the spectrum band and detects the presence or absence of the primary user in the licensed band. Moreover, another serious issue in next generation (5G) wireless communication is to decide the less complex 5G waveform candidate for achieving higher data rate, low latency and better spectral efficiency. Universal filtered multi-carrier (UFMC) is one of the noticeable waveform candidates for 5G and its applications. In this article, we investigate the spectrum sensing methods in multi-carrier transmission for cognitive radio network applications. Especially, we integrate the sensing algorithm into UFMC transceiver to analyze the spectral efficiency, higher data rates and system complexity. Through the simulation results, we prove that the UFMC based cognitive radio applications outperform the existing Orthogonal Frequency Division Multiplexing (OFDM) based CR applications.
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Taufiq, J. A., B. Mellitt, and C. J. Goodman. "Novel algorithm for generating near optimal PWM waveforms for AC traction drives." IEE Proceedings B Electric Power Applications 133, no. 2 (1986): 85. http://dx.doi.org/10.1049/ip-b.1986.0013.
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Živanović, Dragan, Milan Simić, Dragan Denić, and Živko Kokolanski. "SCRIPT FILES APPROACH IN THE POWER QUALITY EVENTS GENERATION." Facta Universitatis, Series: Automatic Control and Robotics 17, no. 2 (December 28, 2018): 93. http://dx.doi.org/10.22190/fuacr1802093z.
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Generation of typical power quality (PQ) events, based on Script files and virtual instrumentation, is presented in this paper. Such approach provides definition and generation of three-phase voltage signals with various PQ events defined according to relevant international quality standards. Using of Script files enables easy and flexible generation of long-time voltage signals with complex PQ disturbances, according to the predefined test algorithms and scenarios. Detailed front panels and block diagrams of developed virtual instrument for signal generation are described in the paper. As specific examples, generated signals defined using the Script files are presented. Experimental confirmation of described software supported method is performed using the three-phase PQ analyzer Fluke 435. Some specific test waveforms and obtained experimental results are shown.
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Liang, Yalin, and Yuyao He. "Simulation research on the grid connected generation system of solar thermal power generation." Thermal Science 24, no. 5 Part B (2020): 3239–48. http://dx.doi.org/10.2298/tsci191125115l.
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Objective: To improve the efficiency and stability of the solar thermal power generation system, and promote the optimization and development of solar thermal power generation grid connection. Methods: The working principle of the heat exchanger in the heat storage system is analyzed. Combined with the technological requirements of the system, the mathematical model of the heat exchanger is established by the mechanism modelling method. According to the inherent characteristics and control requirements of the heat storage system, the control schemes are proposed. The control strategies of different control algorithms, such as single-loop control, Smith predictive compensation control, cascade-Smith control, and feedforward-cascade-Smith control, are designed and adopted. The simulation model is established to obtain step response waveforms of different control systems. The advantages and disadvantages of different control strategies are comprehensively analyzed and compared. Results: After introducing the superheated steam mass-flow disturbance, the error of the single-loop control system increases. After adjusting the system to restore the oscillation state, the system error is high (10.24%). Smith predictive compensation control system fluctuates, with a peak time of 548 seconds
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Ismail, Khaled A., and Mohamed A. Abd El Ghany. "Survey on Machine Learning Algorithms Enhancing the Functional Verification Process." Electronics 10, no. 21 (November 3, 2021): 2688. http://dx.doi.org/10.3390/electronics10212688.
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The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.
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Zghair, Hayder Kadhim, Mehdi Ebady Manaa, Safa Saad A. AL-Murieb, and Fryal Jassim Abd Al-Razaq. "Analysis and description S-box generation for the AES algorithm-a new 3D hyperchaotic system." Bulletin of Electrical Engineering and Informatics 12, no. 3 (June 1, 2023): 1639–47. http://dx.doi.org/10.11591/eei.v12i3.4824.
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In this paper, a description, and analysis of a novel 3-D dimension hyperchaotic system is implemented. The proposed system oscillation is two-order autonomous and consisted of a nine-term and symmetric oscillation w.r.t x-axis. It is proved analysis by Kaplan-York dimension, waveform analysis, phase portrait, and Lyapunov exponent. This work-study stability and equilibrium point and Routh stability criteria produced that the new system has one unstable point from the type saddle-focus point. One of the characteristics of the proposed system is hyperchaotic since this system has two Lyapunov large than zero. This system is applied to generate a chaotic (S-box) based in advanced encryption standard (AES) algorithm for text encryption and gives a high level of security. In addition to the description, and analysis S-box. Therefore. the proposed algorithm is satisfied the high randomness of entropy value and passes the National Institute of Standards and Technology (NIST) parameters and another test. Mathematica and MATLAB programs simulated some results.
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Mallick, Subhashis, and Dwaipayan Chakraborty. "Prediction of the ocean water sound speeds via attribute-guided seismic waveform inversion." GEOPHYSICS 87, no. 3 (March 8, 2022): U67—U79. http://dx.doi.org/10.1190/geo2021-0442.1.
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Oceanic temperature and salinity are important for studying ocean dynamics and marine ecosystems, and for developing climate prediction models. Physical oceanographers measure these properties using expendable instruments, providing accurate measurements at high vertical resolution at sparse lateral locations. Accuracy of these temperature and salinity data when laterally interpolated and used for ocean dynamics, marine biology, and weather-related studies is questionable. Marine seismic data indicate visible reflections within the water columns, which, in turn, are primarily controlled by the vertical and lateral variations of the ocean water sound speeds. Because these sound-speed variations are functions of temperature and salinity, water-column seismic reflection data have been inverted for sound speeds and used to estimate temperature and salinity. These seismically derived temperature and salinity data have lower vertical, but much finer lateral resolution than those measured by expendable instruments, and therefore are useful for physical oceanography, marine biology, and climate research. However, quality of the seismic inversion relies on a good initial model, which requires generating models at sparse locations over 2D or 3D seismic data volumes and interpolating them over the horizon, manually picked from the stacked seismic data. Because horizon picking is subject to human error and bias, replacing it with an automated process is desirable. We outline a new attribute-guided methodology for initial model generation where no horizon picking is necessary. Starting with this initial model, we run prestack waveform inversion based on genetic algorithm optimization and demonstrate that our method can predict reliable sound-speed profiles. We conclude that our method is a good way for estimating ocean water sound speeds and relating them to the temperature and salinity.
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Chen, Jinjie. "A Simulation Research on the Grid-Connected Control Technology of Single-Phase Inverters Based on MATLAB." Journal of Electronic Research and Application 6, no. 4 (July 27, 2022): 7–12. http://dx.doi.org/10.26689/jera.v6i4.4154.
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This paper primarily discusses the main circuit of single-phase inverter circuits. It begins by introducing the research context and the significance of the subject, then discusses the topology of grid-connected single-phase inverter circuits, continues by discussing the control strategy for grid-connected single-phase inverter circuits, realizes a sinusoidal pulse width modulation (SPWM) signal generation circuit and an inverse control algorithm program, and finally ensures good output waveform and fast dynamic response. In view of the hysteresis feature of the grid voltage’s synchronous signal sampling circuit, the acquisition function in digital signal processing (DSP) control chips is applied, and the reasons for the hysteresis phenomenon are thoroughly investigated. The reliability of the SPWM control algorithm is revealed through the results.