Kernel Width Research Articles

A robust maximum correntropy forecasting model for time series with outliers.

It is of great significance to develop a robust forecasting method for time series. The reliability and accuracy of the traditional model are reduced because the series is polluted by outliers. The present study proposes a robust maximum correntropy autoregressive (MCAR) forecasting model by examining the case of actual power series of Hanzhong City, Shaanxi province, China. In order to reduce the interference of the outlier, the local similarity between data is measured by the Gaussian kernel width of correlation entropy, and the semi-definite relaxation method is used to solve the parameters in MCAR model. The results show that the MCAR model in comparison with deep learning methods, in terms of the average value of the mean absolute percentage error (MAPE), performed better by 1.63%. It was found that maximum correntropy is helpful for reducing the interference of outliers.

A Rolling Bearing Fault Diagnosis Method Based on Switchable Normalization and a Deep Convolutional Neural Network

Aiming to address the problems of a low fault detection rate and poor diagnosis performance under different loads and noise environments, a rolling bearing fault diagnosis method based on switchable normalization and a deep convolutional neural network (SNDCNN) is proposed. The method effectively extracted the fault features from the raw vibration signal and suppressed high-frequency noise by increasing the convolution kernel width of the first layer and stacking multiple layers’ convolution kernels. To avoid losing the intensity information of the features, the K-max pooling operation was adopted at the pooling layer. To solve the overfitting problem and improve the generalization ability, a switchable normalization approach was used after each convolutional layer. The proposed SNDCNN was evaluated with two sets of rolling bearing datasets and obtained a higher fault detection rate than SVM and BP, reaching a fault detection rate of over 90% under different loads and demonstrating a better anti-noise performance.

Genome-wide association analysis of spike and kernel traits in the U.S. hard winter wheat.

A better understanding of the genetic control of spike and kernel traits that have higher heritability can help in the development of high-yielding wheat varieties. Here, we identified the marker-trait associations (MTAs) for various spike- and kernel-related traits in winter wheat (Triticum aestivum L.) through genome-wide association studies (GWAS). An association mapping panel comprising 297 hard winter wheat accessions from the U.S. Great Plains was evaluated for eight spike- and kernel-related traits in three different environments. A GWAS using 15,590 single-nucleotide polymorphisms (SNPs) identified a total of 53 MTAs for seven spike- and kernel-related traits, where the highest number of MTAs were identified for spike length (16) followed by the number of spikelets per spike (15) and spikelet density (11). Out of 53 MTAs, 14 were considered to represent stable quantitative trait loci (QTL) as they were identified in multiple environments. Five multi-trait MTAs were identified for various traits including the number of spikelets per spike (NSPS), spikelet density (SD), kernel width (KW), and kernel area (KA) that could facilitate the pyramiding of yield-contributing traits. Further, a significant additive effect of accumulated favorable alleles on the phenotype of four spike-related traits suggested that breeding lines and cultivars with a higher number of favorable alleles could be a valuable resource for breeders to improve yield-related traits. This study improves the understanding of the genetic basis of yield-related traits in hard winter wheat and provides reliable molecular markers that will facilitate marker-assisted selection (MAS) in wheat breeding programs.

A Systemic Investigation of Genetic Architecture and Gene Resources Controlling Kernel Size-Related Traits in Maize.

Grain yield is the most critical and complex quantitative trait in maize. Kernel length (KL), kernel width (KW), kernel thickness (KT) and hundred-kernel weight (HKW) associated with kernel size are essential components of yield-related traits in maize. With the extensive use of quantitative trait locus (QTL) mapping and genome-wide association study (GWAS) analyses, thousands of QTLs and quantitative trait nucleotides (QTNs) have been discovered for controlling these traits. However, only some of them have been cloned and successfully utilized in breeding programs. In this study, we exhaustively collected reported genes, QTLs and QTNs associated with the four traits, performed cluster identification of QTLs and QTNs, then combined QTL and QTN clusters to detect consensus hotspot regions. In total, 31 hotspots were identified for kernel size-related traits. Their candidate genes were predicted to be related to well-known pathways regulating the kernel developmental process. The identified hotspots can be further explored for fine mapping and candidate gene validation. Finally, we provided a strategy for high yield and quality maize. This study will not only facilitate causal genes cloning, but also guide the breeding practice for maize.

Maximum correntropy cubature Kalman filter and smoother for continuous-discrete nonlinear systems with non-Gaussian noises

To estimate the continuous-discrete nonlinear dynamic systems with non-Gaussian non-zero mean noises, a square-root maximum correntropy cubature Kalman filter with adaptive kernel width and its corresponding smoother are proposed in this paper. Based on the statistical linear regression (SLR) technique, the maximum correntropy criterion (MCC) instead of minimum mean square error (MMSE) is applied to our previously derived continuous-discrete Gaussian estimation method. Compared with MMSE, MCC can not only capture the second-order statistical information of non-Gaussian error, but also utilize its higher-order information. In addition, in order to ensure that MCC can work with an appropriate kernel width, an adaptive kernel width adjustment strategy is given by using the sliding window methodology. Further, for a reliable implementation of the proposed continuous-discrete MCC-based algorithms, they are structurally modified into the square root form. The newly proposed approaches are tested and compared with conventional estimation methods in three commonly used numerical applications. Experimental results show that the proposed algorithms are not only accurate and robust, but also have low computational complexities.

An Efficient Parameter Optimization of Maximum Correntropy Criterion

The maximum correntropy criterion (MCC) algorithm depends upon two fundamental parameters, i.e., step-size and kernel width. Previous studies of parameter optimization in the MCC mainly focus on a single parameter (mainly the kernel width), lacking optimization research concerning both parameters. To this end, this letter investigates a novel optimization scheme simultaneously involving step-size and kernel width. The optimization framework is based on making the power of weight error vector undergo the steepest attenuation. Under the premise of maintaining the same evolutionary trend for time-varying step-size and kernel width, we formulate a constrained parameter optimization problem, where the step-size is subject to a kernel width induced constraint. By taking this approach, the original bivariate optimization can be transformed into a univariate optimization problem, which facilitates optimization solving. We further develop an existing reset scheme to make it suitable for kernel width to ensure a good tracking capability. In addition, we investigate the convergence behavior of the optimized algorithm. Simulation results demonstrate that the developed optimization scheme is beneficial for performance improvement, and the resulting algorithm outperforms some state-of-art MCC-based algorithms.

A Variable Parameter LMS Algorithm Based on Generalized Maximum Correntropy Criterion for Graph Signal Processing

The least mean square (LMS) algorithm of the graph signal processing (GSP) based on the mean square error criterion has a poor reconstruction effect when the graph sampling signal is disturbed by impulse noise. To solve this problem, the generalized maximum correntropy criterion (GMCC) is introduced, which is robust to impulse noise in adaptive filtering. Therefore, this paper proposes the GSP LMS algorithm based on the GMCC (GSP LMSGMCC) by using the graph Fourier transform, which has a good effect when the graph sampling signal is disturbed by impulse noise. In addition, the GSP LMSGMCC algorithm based on the fixed parameter including step size and kernel width must make a compromise between convergence speed and steady-state error. To prevent this, the fixed parameters of the proposed GSP LMSGMCC algorithm are optimized, respectively. To facilitate understanding and analysis, the steady-state performance of the proposed GSP LMSGMCC algorithm is studied. Finally, the computer simulations are carried out to verify the superiority of the proposed algorithm when the signals on the graph are static graph signals and streaming graph signals respectively.

Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics

In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human‐computer interaction, machine vision, and other fields.

The influence of Gaussian kernel width on indoor and outdoor radio channels identification from binary output measurements

The influence of Gaussian kernel width on indoor and outdoor radio channels identification from binary output measurements

The influence of Gaussian kernel width on indoor and outdoor radio channels identification from binary output measurements

The influence of Gaussian kernel width on indoor and outdoor radio channels identification from binary output measurements

SEED AND LEAF CHARACTERISTICS OF OLIVE GENOTYPES COLLECTED FROM KAHRAMANMARAS REGION OF TURKEY

Because of it can be used as a table, for oil or wood and practical medicine, olive became highly valued in Mediterranean basin. Olive oil is considered more important due to its nutritional and health advantages. The olive tree can adapt to adverse soil conditions because it is not very selective in terms of soil requirements. Turkey has a very rich diversity because it is located in the homeland of olives and has been cultivated for many years. There are olive populations that show variation in tree form, leaf and fruit characteristics in different regions of Turkey. Identifying and revealing the characteristics of plants in these populations is important for their evaluation. In this study, some morphological features were determined in olive populations in Kahramanmaraş region, which has a significant diversity. Within the olive populations, seed and leaf characteristics were revealed in 44 genotypes that were visually different and over 50 years of age. Statistical differences were found between genotypes in terms of all parameters studied. The kernel weights of the genotypes varied between 0.76-0.38 g. Kernel length and width values were determined as 16.03-12.21 mm and 10.07-8.01 mm, respectively. The highest leaf length of the genotypes was 7.42 cm and the lowest 5.70 cm. Leaf widths were determined as 15.82-8.49 mm. The results revealed that there is a significant olive diversity in Kahramanmaraş region. It is important to conservation this richness and use it in breeding and genetic studies.

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

BackgroundKernel size-related traits, including kernel length (KL), kernel width (KW), kernel diameter ratio (KDR) and kernel thickness (KT), are critical determinants for wheat kernel weight and yield and highly governed by a type of quantitative genetic basis. Genome-wide identification of major and stable quantitative trait loci (QTLs) and functional genes are urgently required for genetic improvement in wheat kernel yield. A hexaploid wheat population consisting of 120 recombinant inbred lines was developed to identify QTLs for kernel size-related traits under different water environments. The meta-analysis and transcriptome evaluation were further integrated to identify major genomic regions and putative candidate genes.ResultsThe analysis of variance (ANOVA) revealed more significant genotypic effects for kernel size-related traits, indicating the moderate to high heritability of 0.61–0.89. Thirty-two QTLs for kernel size-related traits were identified, explaining 3.06%—14.2% of the phenotypic variation. Eleven stable QTLs were detected in more than three water environments. The 1103 original QTLs from the 34 previous studies and the present study were employed for the MQTL analysis and refined into 58 MQTLs. The average confidence interval of the MQTLs was 3.26-fold less than that of the original QTLs. The 1864 putative candidate genes were mined within the regions of 12 core MQTLs, where 70 candidate genes were highly expressed in spikes and kernels by comprehensive analysis of wheat transcriptome data. They were involved in various metabolic pathways, such as carbon fixation in photosynthetic organisms, carbon metabolism, mRNA surveillance pathway, RNA transport and biosynthesis of secondary metabolites.ConclusionsMajor genomic regions and putative candidate genes for kernel size-related traits in wheat have been revealed by an integrative strategy with QTL linkage mapping, meta-analysis and transcriptomic assessment. The findings provide a novel insight into understanding the genetic determinants of kernel size-related traits and will be useful for the marker-assisted selection of high yield in wheat breeding.

Identification of genetic loci for grain yield-related traits in the wheat population Zhongmai 578/Jimai 22

The identification of stable quantitative trait locus (QTL) for yield-related traits and tightly linked molecular markers is important for improving wheat grain yield. In the present study, six yield-related traits in a recombinant inbred line (RIL) population derived from the Zhongmai 578/Jimai 22 cross were phenotyped in five environments. The parents and 262 RILs were genotyped using the wheat 50K single nucleotide polymorphism (SNP) array. A high-density genetic map was constructed with 1 501 non-redundant bin markers, spanning 2 384.95 cM. Fifty-three QTLs for six yield-related traits were mapped on chromosomes 1D (2), 2A (9), 2B (6), 2D, 3A (2), 3B (2), 4A (5), 4D, 5B (8), 5D (2), 7A (7), 7B (3) and 7D (5), which explained 2.7–25.5% of the phenotypic variances. Among the 53 QTLs, 23 were detected in at least three environments, including seven for thousand-kernel weight (TKW), four for kernel length (KL), four for kernel width (KW), three for average grain filling rate (GFR), one for kernel number per spike (KNS) and four for plant height (PH). The stable QTLs QKl.caas-2A.1, QKl.caas-7D, QKw.caas-7D, QGfr.caas-2B.1, QGfr.caas-4A, QGfr.caas-7A and QPh.caas-2A.1 are likely to be new loci. Six QTL-rich regions on 2A, 2B, 4A, 5B, 7A and 7D, showed pleiotropic effects on various yield traits. TaSus2-2B and WAPO-A1 are potential candidate genes for the pleiotropic regions on 2B and 7A, respectively. The pleiotropic QTL on 7D for TKW, KL, KW and PH was verified in a natural population. The results of this study enrich our knowledge of the genetic basis underlying yield-related traits and provide molecular markers for high-yield wheat breeding.

Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Motor Based on Least Squares Support Vector Machine Generalized Inverse Optimized by Improved Genetic Algorithm

In the decoupling control process of the outer rotor coreless bearingless permanent magnet synchronous motor using a least squares support vector machine, the kernel width <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">σ</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and the penalty factor <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</i> of the least squares support vector machine are difficult to tune, resulting in high kernel space complexity and low fitting accuracy. A new improved genetic algorithm is used to optimize the above parameters. First, the generalized inverse system model of the original nonlinear multiple-input multiple-output system is fitted online with the optimized least squares support vector machine, and the generalized inverse system model is connected in series with the original system to form a pseudolinear system. Then, a linear closed-loop controller is used for control. Simulations and experiments show that the robustness and wide applicability of the original system are guaranteed. The dynamic and static performances of the system are improved, verifying the effectiveness of the scheme.

Diversity of twenty-three sweet corn (Zea mays L. saccharata) varieties in Indonesia

Abstract. Heryanto FSS, Wirnas D, Ritonga AW. 2022. Diversity of twenty-three sweet corn (Zea mays L. saccharata) varieties in Indonesia. Biodiversitas 23: 6075-6081. One of the most well-known vegetables in Asian countries, including Indonesia. Improving superior sweet corn varieties is important in the face of increasing biotic or abiotic stresses and consumer preferences for agricultural products. Plant breeding programs rely on genetic variability and heritability information. Therefore, this study aimed to quantify the genetic diversity and heritability of 23 commercial sweet corn varieties in Indonesia. The study was conducted at the Leuwikoppo experimental field of the Department of Agronomy, Faculty of Agriculture, IPB University, Bogor, West Java, Indonesia, from August to November 2021. Twenty-three commercial sweet corn varieties (hybrid and open-pollinated) from Indonesia were planted in the field employing a randomized complete block design with 3 replications. Ear shelf life and green ear yield had a high genotypic coefficient of variation (GCV), whereas plant height, ear height, stalk length, 100-grain weight, total soluble solids, green ear shelf life, green ear weight, and ear weight had a moderate GCV. Stem diameter, leaf number, leaf length, leaf width, 50% days to tasseling and silking, anthesis, silking interval, ear length, ear diameter, ear length with the kernel, ear length percent with the kernel, kernel length, and kernel width had a low GCV. High genetic diversity was present among the trialed varieties, indicating their high potential for improvement.

Unlocking the genetic control of spring wheat kernel traits under normal and heavy metals stress conditions

Background and aimsPb and Sn concentration increase rapidly due to the industrial revolution and cause a significant reduction in wheat production and productivity. Understanding the genetic control of Pb and Sn tolerance is very important to produce wheat cultivars that are tolerant to such metals.MethodsExtensive genetic analyses using genome-wide association study, functional annotation, and gene enrichment were investigated in a set of 103 highly diverse spring wheat genotypes. Kernel traits such as kernel length (KL), kernel diameter (KD), kernel width (KW), and 1000-kernel weight (TKW) were measured under each metal as well as under controlled conditions.ResultsThe GWAS identified a total of 131, 126, and 115 markers that were associated with kernel traits under Ctrl, Pb, and Sn. Moreover, the stress tolerance index (STI) for Pb and Sn was calculated and GWAS revealed 153 and 105 significant markers, respectively. Remarkably, one SNP Ku_c269_2643 located within TraesCS2A02G080700 gene model was found to be associated with KL under the three conditions. The results of gene enrichment revealed three, three, and six gene networks that have an association with the processes involved in kernel formation. The target alleles of all significant markers detected by GWAS were investigated in the most tolerant wheat genotypes to truly select the candidate parents for crossing in future breeding programs.ConclusionThis is the first study that unlocked the genetic control of kernel yield under controlled and heavy metals conditions. Understanding the genetic control of kernel traits under heavy metals will accelerate breeding programs to improve wheat tolerance to Pb and Sn.

Generalized complex kernel least-mean-square algorithm with adaptive kernel widths

Generalized complex kernel least-mean-square algorithm with adaptive kernel widths

Detection Method for Walnut Shell-Kernel Separation Accuracy Based on Near-Infrared Spectroscopy.

In this study, Near-infrared (NIR) spectroscopy was adopted for the collection of 1200 spectra of three types of walnut materials after breaking the shells. A detection model of the walnut shell-kernel separation accuracy was established. The preprocessing method of de-trending (DT) was adopted. A classification model based on a support vector machine (SVM) and an extreme learning machine (ELM) was established with the principal component factor as the input variable. The effect of the penalty value (C) and kernel width (g) on the SVM model was discussed. The selection criteria of the number of hidden layer nodes (L) in the ELM model were studied, and a genetic algorithm (GA) was used to optimize the input layer weight (W) and the hidden layer threshold value (B) of the ELM. The results revealed that the classification accuracy of SVM and ELM models for the shell, kernel, and chimera was 97.78% and 97.11%. The proposed method can serve as a reference for the detection of walnut shell-kernel separation accuracy.

Microwave-ultrasound assisted extraction of red corn pigments and their effect on chemical composition and tecno-functional properties

Currently, there is a growing interest in obtaining natural pigments, without presenting a risk to health, as well as to the matrix and food safety. The objective of this study was to characterize grains with different shades of red color (High pigmentation maize = RA, Medium pigmentation maize = RM, and Low pigmentation maize = RB, reddish maize with white segments = VO and white maize control = SC). The physical properties (kernel weight, length, width, thickness, and grain and flour color), chemical composition (moisture, fat, protein, fiber, starch, ash, and minerals), and techno-functional properties (capacity of absorption of organic molecules (OMAC), swelling (CH), oil retention (CAAC), and water absorption (WAS)) variables were measured. Afterward, RM was selected, and a pigment extraction (EAUM) was carried out using microwave-ultrasound equipment with different ratios of ethanol (0, 30, and 70%) and citric acid ratios (0 and 1%). The color and properties of the solid (starch, protein, fat, and their techno-functional properties) were determined in the aqueous phase and were compared with the properties prior to EAUM. The results indicated that the red grains have higher starch content (57.6%) and reddish colors, in addition, VO presented higher moisture (10.4%), fat (6.7%) and fiber (10.0%). The colors of the pigments obtained with EtOH (30 and 70%) show shades red and yellow. Finally, the EAUM decreased the fat content up to 57%, increased WAS and CAAC in the residual flour and generated changes in OMAC and CH, increasing in aqueous medium and reducing with EtOH.

Evaluation of synthetic hexaploid wheat (Triticum aestivum) derived RILs for kernel traits

In bread wheat (Triticum aestivum L.), kernel parameters are significant grain quality characteristics that affect yield and milling quality as well as market price. Utilization of synthetic hexaploid donor to harness the diverse alleles for broadening the genetic base is a novel breeding strategy of the bread wheat breeding programs across the globe. An experiment was conducted during winter (rabi) season 2017–18 and 2018–19 at the research farm of ICAR-Indian Agricultural Research Institute, New Delhi to evaluate 188 recombinant inbred lines (RILs) which were derived from HD 2932 (Indian bread wheat cultivar) and Synthetic 46 (Syn 46: Synthetic hexaploid donor) to identify the effect of the synthetic hexaploid donor on kernel parameters. Kernel parameters like kernel length (KL), kernel width (KW), kernel thickness (KT) and thousand kernel weight (TKW) were studied. Syn 46 had larger kernels and a high TKW with respect to HD 2932. Significant variation for all the traits was observed in the population with transgressive segregants. In this RILs population (188) KL ranged from 5.13–7.5 mm, KW ranged of 2.26–3.9 mm, KT ranged from 2.57–3.41 mm, and of TKW ranged from 25.2–53.17 g. Correlation analysis showed a significant positive correlation of TKW with KL and KT. Potential RILs identified with desirable allelic combinations may be used for future breeding programs, and RILs with contrasting kernel parameters may be used to create nested RILs for further generation of variability.