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This article provides an overview of phase-locked loops (PLLs) and their significance in commercial and military products. It focuses on phase noise, a key concern in PLL performance, and explains the factors that contribute to it. The article also discusses the theory and measurements of phase noise in both the time and frequency domains. It aims to offer a comprehensive understanding of phase noise and its impact on PLLs. Additionally, the document explains the measurement and analysis of phase noise in the frequency domain, including the equations used and the differences between baseband and radio frequency measurements. It also covers the analysis procedure for modeling and simulating phase noise in a PLL. [Extracted from the article]

Shrimp tends to deteriorate during the refrigeration process. To monitor the freshness of shrimp during refrigeration, near-infrared (NIR) hyperspectral imaging was utilized to non-destructively identify the freshness of shrimp. In the process, three preprocessing methods (multivariate scatter correction [MSC], standard normal variate [SNV], and direct orthogonal signal correction [DOSC]) were employed to preprocess the full-wavelength spectral data, and three characteristic wavelength extraction algorithms (competitive adaptive reweighted sampling [CARS], and random forest [RF] simulated annealing [SA]) were used to extract the best-pre-processed data. Because extreme learning machine (ELM) and kernel extreme learning machine (KELM) are easily affected by parameters, ELM (based on teaching-learning-based optimization [TLBO]) and KELM (based on teaching-learning-based optimization [TLBO]) were proposed. In this study, four discriminant models (ELM, TLBO– ELM, KELM, and TLBO–KELM) were used for the full wavelength modeling analysis and the characteristic wavelength modeling analysis. In this work, the results of the final selected models are presented. [ABSTRACT FROM AUTHOR]

Lignocellulose determination and categorization analysis are critical to the treatment of biofuel pellets. For lignocellulose determination, partial least square (PLS) regression models based on full-range spectra, feature intervals, and feature bands were established. The models that were based on feature bands displayed the best performance, with the determination coefficients of 0.956, 0.864, and 0.926 for cellulose, hemicellulose, and lignin, respectively. For the categorization analysis issue, two specific cases were studied. First, linear discriminant analysis and random forest were used for biofuel pellet classification. Second, fuzzy clustering based on transitive closure was adopted to implement the hierarchical clustering of biofuel pellets without the label information. Excellent categorization results were obtained. This paper provides an effective auxiliary means for the pretreatment and storage of biofuel pellets with the utilization of Fourier transform infrared (FT-IR) spectra. [ABSTRACT FROM AUTHOR]

The article focuses on avoiding common data preparation mistakes to improve analytics results. Topics discussed include time series data preparation in process manufacturing applications presents complex challenges; and modeling relates to a variety of techniques, including regression or clustering algorithms, along with more complex machine learning or artificial intelligence models.

The article focuses on the implementation of fast surrogate machine learning (ML) to replace slow finite element (FE) simulation tools. Topics discussed include the ability of FE models to generate large data for such ML models as neural networks (NN), random forest models or Gaussian Process Regression (GPR) models, simulation speed offered by the model, and use of the surrogate NN models in composites producibility assessment-thermal analysis (CPA-TA) by Convergent Manufacturing Technologies.

The article focuses on some of the ways the artificial intelligence (AI) algorithms that are used in HVAC control systems use data to approximate functions. Topics include the AI algorithms used in our industry could be grouped into two types: classification and regression, and the algorithms are used to predict continuous variables are chiller plant energy consumption, cooling coil leaving air temperature, etc.

The article focuses on the research done to use machine learning algorithms in predicting best conditions for a chemical reaction, even when the substrate has never been used before. Topics discussed include prediction of reactivity after Buchwald-Hartwig amination, decision making using random forest model, and facility for homogenous catalysis and chemical synthesis.