Kernel Processing Research Articles

Run-indexed time-varying Bayesian optimization with positional encoding for auto-tuning of controllers: Application to a plasma-assisted deposition process with run-to-run drifts

Bayesian optimization (BO) has emerged as a useful paradigm for automatic calibration (aka auto-tuning) of advanced optimization- and learning-based controllers whose closed-loop performance depends on the choice of several tuning parameters in highly nonlinear and nonconvex ways. However, BO approaches to controller auto-tuning commonly rely on the assumption that system dynamics remain constant, which does not hold for systems with time-varying dynamics, for example, due to gradual aging or persistent environmental drifts. This challenge can be further compounded when gradual and persistent system drifts occur over a series of process runs. Existing time-varying BO (TVBO) approaches with spatio-temporal kernels fall short of effectively handling an integer run index, which is imperative for capturing run-to-run changes in the system behavior. To this end, this paper presents a run-indexed TVBO (RI-TVBO) approach that can systematically account for run-to-run process drifts as the system is queried over sequential process runs. The proposed approach relies on adapting the non-stationary Wiener process kernel to accommodate an integer run index, instead of time. This is done via positional encoding that incorporates the integer run index and, thus, enables describing run-to-run variations in system dynamics. The positional embedding vector associated with each run index is then mapped onto a scalar value to leverage the relationships between different process runs within the probabilistic surrogate model of the objective function in RI-TVBO. The performance of RI-TVBO is evaluated for auto-tuning of an offset-free model predictive controller for a low-temperature plasma-assisted process for thin film deposition. Simulation results demonstrate the superior performance of RI-TVBO over standard BO and TVBO under different scenarios of run-to-run process drifts encountered in plasma-assisted deposition processes in semiconductor manufacturing.

Non-destructive prediction of hazelnut and hazelnut kernel deformation energy using machine learning techniques

ABSTRACT The hazelnut possesses a significant economic value and is extensively consumed on a global scale. Physico-mechanical properties such as linear dimensions, deformation, force, stress, and energy play an important role in the processing of hazelnut and hazelnut kernels, quality assessment, and the development of harvesting and post-harvest technologies. The data used in the data set was determined by applying compression tests and artificial neural networks, support vector regression, and multiple linear regression methods were applied to the data obtained. The aim of the study ws to determine the deformation energy of hazelnuts and hazelnut kernels based on some mechanical properties of hazelnuts using nondestructive machine learning methods instead of traditional measurement methods with minimum error, minimum labor, and in the shortest time. The average R2 for kernels and hazelnuts was ANN 95.2%, SVR 89.6%, and MLR 86.1%. The average MSE for kernels and hazelnuts was ANN 0.006, SVR 0.012, and MLR 0.072. The machine learning methods used in the study provided results close to the ideal statistical metrics. According to the analyses of the machine learning methods, results similar to the optimal statistical metrics were obtained. The most successful and least-error methods were the artificial neural network, support vector regression and multiple linear regression, respectively.

Uniform approximation of common Gaussian process kernels using equispaced Fourier grids

The high efficiency of a recently proposed method for computing with Gaussian processes relies on expanding a (translationally invariant) covariance kernel into complex exponentials, with frequencies lying on a Cartesian equispaced grid. Here we provide rigorous error bounds for this approximation for two popular kernels—Matérn and squared exponential—in terms of the grid spacing and size. The kernel error bounds are uniform over a hypercube centered at the origin. Our tools include a split into aliasing and truncation errors, and bounds on sums of Gaussians or modified Bessel functions over various lattices. For the Matérn case, motivated by numerical study, we conjecture a stronger Frobenius-norm bound on the covariance matrix error for randomly-distributed data points. Lastly, we prove bounds on, and study numerically, the ill-conditioning of the linear systems arising in such regression problems.

Expressive mortality models through Gaussian process kernels

AbstractWe develop a flexible Gaussian process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative structure of GP kernels, we design a genetic programming algorithm to search for the most expressive kernel for a given population. Our compositional search builds off the Age–Period–Cohort (APC) paradigm to construct a covariance prior best matching the spatio-temporal dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA) on synthetic case studies to validate the ability of the GA to recover APC structure and on real-life national-level datasets from the Human Mortality Database. Our machine learning-based analysis provides novel insight into the presence/absence of Cohort effects in different populations and into the relative smoothness of mortality surfaces along the Age and Year dimensions. Our modeling work is done with the PyTorch libraries in Python and provides an in-depth investigation of employing GA to aid in compositional kernel search for GP surrogates.

Analisa Kinerja Mesin Ripple Mill dengan Beban 30 Ton/Jam

The growth of oil palm agricultural land until 2017 was 234 479. Ha, with a production productivity figure of 437 292 tons. (BPS 2017) The development of oil palm plants has been developed in several regions in Indonesia and has become a superior plantation crop. Because it has quite high economic value. One factor that needs to be considered in the company's progress is the issue of smoothness. The production process is greatly influenced by the performance of the machines used. PT. Merbaujaya Indahraya is a subsidiary company of PT. MIG is engaged in processing fresh palm fruit bunches into Crude Palm Oil and Palm Kernel Oil. Fresh fruit bunches are obtained from core gardens and farmers. Core garden with 4 sections of MI Group gardens (MI, Binanga Mandala and Heksa). with a capacity of 30 tons/hour. Processing palm oil into CPO (Crude Palm Oil) has several stations including Loading ramp, Sterilizer, Thresser, Screw Press, clarification, and Kernel. The ripple mill machine is a palm kernel processing machine that is located at the kernel station and is prone to damage, where in the ripple mill machine, the seeds enter between the rotor and the ripple plate being pressed so that they collide with each other and break the shell from the core. So that the core is separated from the shell. This research aims to analyze the performance of the ripple mill machine. From the results of the analysis, an efficiency level of 80% was obtained, with 4 samples taken and varying collection times of around 2 hours within 1 (one) day of production.

Processing and disposal of cashew kernels in south Konkan region: An economic analysis

Processing and disposal of cashew kernels in south Konkan region: An economic analysis

Neural Network Gaussian Processes by Increasing Depth.

Recent years have witnessed an increasing interest in the correspondence between infinitely wide networks and Gaussian processes. Despite the effectiveness and elegance of the current neural network Gaussian process theory, to the best of our knowledge, all the neural network Gaussian processes (NNGPs) are essentially induced by increasing width. However, in the era of deep learning, what concerns us more regarding a neural network is its depth as well as how depth impacts the behaviors of a network. Inspired by a width-depth symmetry consideration, we use a shortcut network to show that increasing the depth of a neural network can also give rise to a Gaussian process, which is a valuable addition to the existing theory and contributes to revealing the true picture of deep learning. Beyond the proposed Gaussian process by depth, we theoretically characterize its uniform tightness property and the smallest eigenvalue of the Gaussian process kernel. These characterizations can not only enhance our understanding of the proposed depth-induced Gaussian process but also pave the way for future applications. Lastly, we examine the performance of the proposed Gaussian process by regression experiments on two benchmark datasets.

A NEW APPROACH FOR SPEECH EMOTION RECOGNITION USING SINGLE LAYERED CONVOLUTIONAL NEURAL NETWORK

Creating a computational device to identify human emotions via voice analysis represents a notable achievement in the sector of human-computer interaction, especially within the healthcare domain. We propose a new light-weight model for addressing challenges of emotions recognition. The model works based on CNN with change of kernel processing. The proposed model performs a direct matching to recognize speech emotions of different eight categories using a statistical model named Analysis of Variance (ANOVA) as kernel for features extraction and Cosine Similarity Measurement (CSM) as activation function for CNN model. This proposed model contains eight-folded single-layered intermediate neurons, and each neuron can segregate speech emotion pattern using CSM from the voice convergence matrix to explore a part of the solution from the whole solution. Experiment results demonstrates that the proposed model outperforms compared with multiple layered existing CNN methods in identifying the emotional state of a speaker.

A NEW APPROACH FOR SPEECH EMOTION RECOGNITION USING SINGLE LAYERED CONVOLUTIONAL NEURAL NETWORK

Creating a computational device to identify human emotions via voice analysis represents a notable achievement in the sector of human-computer interaction, especially within the healthcare domain. We propose a new light-weight model for addressing challenges of emotions recognition. The model works based on CNN with change of kernel processing. The proposed model performs a direct matching to recognize speech emotions of different eight categories using a statistical model named Analysis of Variance (ANOVA) as kernel for features extraction and Cosine Similarity Measurement (CSM) as activation function for CNN model. This proposed model contains eight-folded single-layered intermediate neurons, and each neuron can segregate speech emotion pattern using CSM from the voice convergence matrix to explore a part of the solution from the whole solution. Experiment results demonstrates that the proposed model outperforms compared with multiple layered existing CNN methods in identifying the emotional state of a speaker.

Research on laser induced plasma ignition of gas oxygen/methane

Laser-induced plasma ignition is a new ignition method to achieve stable and reliable ignition of liquid rocket engines. It has the advantages of accurate and controllable ignition position, broadening ignition lean-burn limit and small disturbance to flow field. In this paper, the laser ignition process and combustion characteristics of gas oxygen/gas methane under rocket engine conditions are studied from four aspects, namely different ignition positions, ignition energy, fuel-oxygen momentum ratio and chamber pressure. Using a high-speed camera, the dynamic process of ignition was captured by the shadow method, and the generation and propagation of laser-induced flame nuclei were recorded. The results show that at the airflow velocity of 300–400 m/s, the time required for the flame kernel to develop to the entire combustion chamber is about 300 μs, and the time for the flame to reach a steady state is tens of milliseconds. When the ignition energy is increased by 1.5 times, the time required for the flame kernel to develop from the recirculation zone to the mainstream zone is shortened to 1/3 of the original; with the increase of the fuel-oxygen momentum ratio, the shape of the initial flame kernel changes from round to oval, and the development speed of the flame kernel also increases. The fuel-oxygen momentum ratio increases from 0.59 to 2.75, and the time required for the flame kernel to develop from the recirculation zone to the mainstream zone decreases from 167 μs to 56 μs＀ Except chamber pressure, other factors have no effect on the pressure rise rate of the ignition process, and the higher the chamber pressure, the faster the pressure rise rate; in order to avoid deflagration, the shear layer is more suitable as the ignition position. With the increase of ignition energy, fuel-oxygen momentum ratio and chamber pressure, the development speed of flame kernel increases, but the shape and development process of flame kernel will be different under the influence of different factors.

Machine Learning-Based predictions of crack growth rates in an aeronautical aluminum alloy

Recent advancements in computational capabilities have made data-driven approaches increasingly valuable for accurately simulating complex engineering phenomena. These approaches provide mathematical approximations that serve as surrogate models, replacing complex explicit mathematical relationships with significantly reduced computational demands. In this study, we propose the development of a surrogate model to predict the crack growth behavior of aluminum 7075-T6, a commonly used material in the aviation industry. The crack growth behavior of this alloy is complex and challenging to describe with explicit mathematical equations due to multiple influencing factors. Notably, the “waviness” behavior observed in the linear Paris law region, coupled with the significant impact of the R-ratio and specimen thickness, complicates crack growth predictions. To address this complexity, four different machine learning regression algorithms are studied: Random Forest (RF), Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), and Kernel Regression (KR). Their performance is evaluated and the most promising algorithm is selected to predict a crack growth benchmark problem of a central crack in aluminum 7075-T6 plate under various fatigue stress spectra.

Design and decoding of polar codes with large kernels: a survey

We present techniques for the construction of polar codes with large kernels and their decoding. A crucial problem in the implementation of the successive cancellation decoding algorithm and its derivatives is kernel processing, i.e., fast evaluation of the log-likelihood ratios for kernel input symbols. We discuss window and recursive trellis processing methods. We consider techniques for evaluation of the reliability of bit subchannels and for obtaining codes with improved distance properties.

A Bayesian method to mitigate the effects of unmodelled time-varying systematics for 21-cm cosmology experiments

ABSTRACT Radio observations of the neutral hydrogen signal from the Cosmic Dawn and Epoch of Reionization have helped to provide constraints on the properties of the first stars and galaxies. Since this global 21-cm cosmological signal from the Cosmic Dawn is effectively constant on observing time-scales and since effects resulting from systematics will vary with time, the effects of these systematics can be mitigated without the need for a model of the systematic. We present a method to account for unmodelled time-varying systematics in 21-cm radio cosmology experiments using a squared exponential Gaussian process kernel to account for correlations between time bins in a fully Bayesian way. We find by varying the model parameters of a simulated systematic that the Gaussian process method improves our ability to recover the signal parameters by widening the posterior in the presence of a systematic and reducing the bias in the mean fit parameters. When varying the amplitude of a model sinusoidal systematic between 0.25 and 2.00 times the 21-cm signal amplitude and the period between 0.5 and 4.0 times the signal width, we find on average a 5 per cent improvement in the root mean squared error of the fitted signal. We can use the fitted Gaussian process hyperparameters to identify the presence of a systematic in the data, demonstrating the method’s utility as a diagnostic tool. Furthermore, we can use Gaussian process regression to calculate a mean fit to the residuals over time, providing a basis for producing a model of the time-varying systematic.

Retrieving the 21-cm signal from the Epoch of Reionization with learnt Gaussian process kernels

ABSTRACT Direct detection of the Cosmic Dawn and Epoch of Reionization via the redshifted 21-cm line of neutral Hydrogen will have unprecedented implications for studying structure formation in the early Universe. This exciting goal is challenged by the difficulty of extracting the faint 21-cm signal buried beneath bright astrophysical foregrounds and contaminated by numerous systematics. Here, we focus on improving the Gaussian Process Regression (GPR) signal separation method originally developed for LOFAR observations. We address a key limitation of the current approach by incorporating covariance prior models learnt from 21-cm signal simulations using variational autoencoder (VAE) and interpolatory autoencoder (IAE). Extensive tests are conducted to evaluate GPR, VAE–GPR, and IAE–GPR in different scenarios. Our findings reveal that the new method outperforms standard GPR in component separation tasks. Moreover, the improved method demonstrates robustness when applied to signals not represented in the training set. It also presents a certain degree of resilience to data systematics, highlighting its ability to effectively mitigate their impact on the signal recovery process. However, our findings also underscore the importance of accurately characterizing and understanding these systematics to achieve successful detection. Our generative approaches provide good results even with limited training data, offering a valuable advantage when a large training set is not feasible. Comparing the two algorithms, IAE–GPR shows slightly higher fidelity in recovering power spectra compared to VAE–GPR. These advancements highlight the strength of generative approaches and optimize the analysis techniques for future 21-cm signal detection at high redshifts.

162 Kernel Processing Corn Silage Included at 65% of the Diet (DM Basis) and fed to Growing Beef Steers Increases dry Matter Intake, Decreases Apparent Total Tract Digestibility of Neutral Detergent Fiber, and Does not Enhance Starch Digestibility

Abstract The objective of this study was to evaluate the effects of kernel processing of corn silage on apparent total tract digestibility (ATTD) of steers fed corn silage included at 65% of the diet (DM basis). Steers (n = 184; initial shrunk BW = 338 ± 22.3 kg) were assigned to 1 of 24 pens (8 steers/pen). Pens were randomly assigned to one of two dietary treatments (12 pens/treatment): non-kernel processed corn silage included at 65% diet DM (CON) or kernel processed corn silage included at 65% diet DM (KPCS) in a randomized complete block design with pen as experimental unit. Steers from replicates 1 to 8 spent 47-d on test and steers from replicates 9 to 12 were fed for 46-d on test. On the final day of the study, apparent total tract digestibility of diet dry matter (DM), organic matter (OM), neutral detergent fiber (NDF), N, and starch was determined using an internal marker ratio technique using the undigested NDF residue following a 240 h in-situ incubation in ruminal fluid (uNDF240). Diet samples were collected twice daily (morning and afternoon) starting two days prior to fecal collections. Fecal collections were taken via rectal palpation on the final day of the study. Data were analyzed as a RCBD with fixed effects of KPCS diet and random effect of block (location within feedyard). Kernel processing increased DMI by 6% (P = 0.01) during the collection period and cumulative DMI throughout the study was increased by 2% for KPCS treatment (P = 0.04). Kernel processing did not enhance the ATTD of DM, OM, or starch (P ≥ 0.71), but did decrease ATTD of NDF by approximately 6% (P = 0.04) compared with corn silage that was not kernel processed. Furthermore, kernel processing was determined to increase ATTD of N by 5% (P = 0.05). These results indicate that kernel processed corn silage may decrease apparent total tract digestibility of neutral detergent fiber due to greater intake but does not appreciably influence starch digestibility.

Bond Parameter Calibration and Crushing Process Analysis of Brown Rice Kernels

Aiming to resolve the practical problem of brown rice kernels being easily broken due to overprocessing during processing (milling and polishing), brown rice kernels of Japonica rice, after hulling, were used as the research object in this study. Firstly, through a texture meter test, the discrete element bonding parameters (Kn is normal stiffness per unit area, Kτ is tangential stiffness per unit area, Cn is critical normal stiffness, Cτ is critical shear stiffness) were obtained. Secondly, a brown rice kernels’ bonding particle model was established by EDEM, and then a second orthogonal rotational combination test was carried out to calibrate the discrete bonding parameters, Kn = 4.43 × 1012 N/m3, Kτ = 6.13 × 1011 N/m3, Cn = 2.55 × 107 Pa, and Cτ = 7.92 × 107 Pa. The error of parameter calibration was within 5%, and the results were able to reflect the actual situation more realistically. Finally, analysis of the crushing process of brown rice kernels showed that their ability to withstand shear damage was not as great as their pressure-bearing capacity. The design of the relevant mechanism and the setting of parameters should be based on the critical shear stiffness of brown rice kernels, and the actual shear force Fτ* set during the processing should be smaller than the theoretical critical shear force Fτ (Fτ* < Fτ = 9.11 N). This study can provide a theoretical basis for optimizing the key structure and operating parameters of rice milling machines and polishing machines to effectively solve the practical problem of increased crushing of brown rice kernels due to overprocessing.

A deep implicit memory Gaussian network for time series forecasting

In recent years, significant achievements have been made in time series forecasting using deep learning methods, particularly the Long Short-Term Memory Network (LSTM). However, time series often exhibit complex patterns and relationships like trends, seasonal patterns and irregularities, and LSTM networks fail to effectively strengthen long-term dependence in time series data, which may lead to inaccurate forecasting effect or performance degradation. Therefore, building a model to explore the temporal dependence within the time series data completely still remains a challenge. In this paper, we propose a novel Deep Implicit Memory Gaussian (DIMG) Network based on bidirectional deep memory kernel process and the implicit features enhancement method for time series forecasting. We first use the implicit features enhancement method to obtain the hidden features of the data according to the nonlinear mapping characteristics of encoder. Then, a new deep learning process called bidirectional deep memory kernel has been developed, which merges the structural properties of deep learning with the adaptability of kernel methods to capture intricate information and memory structures in sequential data. This process fully encapsulates the structure of Bi-LSTM and Gaussian process regression (GPR). Finally, the performance of the proposed model is evaluated on two real-world datasets. The experimental results verify that our model outperforms other reported methods in terms of prediction accuracy.

Enhancing estimation accuracy of nonstationary hydrogeological fields via geodesic kernel-based Gaussian process regression

In this study, the combined application of the geodesic kernel and Gaussian process regression was explored to estimate nonstationary hydraulic conductivity fields in two-dimensional hydrogeological systems. Specifically, a semianalytical form of the geodesic distance based on the intrinsic geometry of the manifold was derived and used to define positive definite geodesic covariance matrices that were used for the Gaussian process regression. Furthermore, the proposed approach was applied to a series of synthetic hydraulic conductivity estimation problems. The results show that the incorporation of secondary information, such as interpretations of geophysical explorations or geological surveys, can considerably improve the accuracy of the estimation, especially in nonstationary fields. In addition, groundwater flow and solute transport simulations based on estimated hydraulic conductivity fields reveal that the accuracy of the simulation is strongly affected by the inclusion of secondary information. These results suggest that incorporating secondary information into manifold geometry can remarkably improve the accuracy of the estimation and provide new insights into the underlying structure of geological data. This proposed approach has critical implications for hydrogeological applications, such as groundwater resource management, safety assessments, and risk management strategies related to groundwater contamination.

Estimation of canopy water content for wheat through combining radiative transfer model and machine learning

Canopy water content (CWC) is an important indicator of crop growth. Currently, the hybrid approach, which combines the physical model and machine learning (ML) method, is commonly used for inversing CWC at the regional scale via optical imagery. Although this approach has good inversion accuracy, it suffers from ill-posed issues from the physical model due to the difference between actual and simulated scenes. Meanwhile, the ability of existing ML methods to resolve this difference remains unclear. To fill the above study gaps, we added different degrees of Gaussian noise into the simulated dataset to reduce the differences in spectral reflectance between that simulated by the PROSAIL-5B model and that measured from optimal imagery. Furthermore, this study also compared the performance of different ML approaches (neural network, NN; Gauss process regression, GPR; and kernel ridge regression, KRR) in the retrieval of wheat (Triticum aestivum L.) CWC based on PROSAIL-simulated datasets. Additionally, we also compared the inversion results for the CWC generated by the SNAP Toolbox with those estimated by the hybrid approach. The results showed that the aforementioned hybrid approaches performed well in the retrieval of wheat CWC when Gaussian noise with a mean of zero and a standard deviation of 0.06 (η: 0, 0.06) was added to the simulated dataset. The hybrid model based on GPR had the highest accuracy of estimation for the CWC (RMSE = 0.0096 g/cm2, RRMSE = 22.2%), when compared with NN or KRR (NN: RMSE = 0.0105 g/cm2, RRMSE = 26.9%; KRR: RMSE = 0.0099 g/cm2, RRMSE = 24.3%). It also outperformed the existing CWC-retrieval algorithms among the SNAP Toolbox (RMSE = 0.027 g/cm2, RRMSE=44.1%). Our results demonstrate that the hybrid approach in combination with GPR and the physical model can accurately retrieve the CWC of crops, thus confirming that the approach is suitable for estimating the CWC.

StateOS: A Memory-Efficient Hybrid Operating System for IoT Devices

The increasing significance of operating systems (OSs) in the development of the internet of things (IoT) has emerged in the last decade. An event-driven OS is memory efficient and suitable for resource-constrained IoT devices and wireless sensors, although the program’s control flow, which is determined by events, is not always obvious. A multithreaded OS with sequential control flow is often considered clearer. However, this approach is memory-consuming. A hybrid OS seeks to combine the strengths of the event-driven approach with multithreaded approach. An event-driven cooperative threaded OS represents a hybrid approach that supports concurrency by explicitly yielding control to another thread. Although this approach is memory efficient, as cooperative threads are not preemptive, it may not provide sufficient real-time performance. This article proposes a memory-efficient hybrid OS, called StateOS, for resource-constrained IoT devices. It is an event-driven cooperative threaded OS with partial real-time performance. StateOS implements a hybrid task scheduler that combines two cooperative threaded subsystems as kernel processes on a priority-based preemptive scheduler. This approach provides adequate real-time performance for IoT devices at a low memory cost.