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About 2,846 papers for “kernel configuration”

Configuration of the CTRON* Kernel

Toshikazu Ohkubo, Tetsuo Wasano, I. Kogiku · IEEE Micro · 1987

TRON's specification for a communication network operating system produced this kernel model. It promises software portability and a high level of performance functions common to diverse network nodes.

Cited by 25

Configuration and Compiling of Linux Kernel

HU Qing-lie · 2009

Linux is a very practical modern operating system,which opens source coding and allows the user to upgrade its kernel.In the environment of Redhat 7.2,the paper analysis the Linux 2.4.18 version of kernel configuration, compiling and new kernel process switch- ing,and so on.

Cited by 0

On the Effects of Kernel Configuration in Multi-Kernel Polar Codes

Souradip Saha, Luis Maßny, Marc Adrat et al. · Entropy · 2022

Polar codes are a relatively new family of linear block codes which have garnered a lot of attention from the scientific community, owing to their low-complexity implementation and provably capacity achieving capability. They have been proposed to be used for encoding information on the control channels in 5G wireless networks due to their robustness for short codeword lengths. The basic approach introduced by Arikan can only be used to generate polar codes of length N=2n, ∀n∈N. To overcome this limitation, polarization kernels of size larger than 2×2 (like 3×3, 4×4, and so on), have already been proposed in the literature. Additionally, kernels of different sizes can also be combined together to generate multi-kernel polar codes, further improving the flexibility of codeword lengths. These techniques undoubtedly improve the usability of polar codes for various practical implementations. However, with the availability of so many design options and parameters, designing polar codes that are optimally tuned to specific underlying system requirements becomes extremely challenging, since a variation in system parameters can result in a different choice of polarization kernel. This necessitates a structured design technique for optimal polarization circuits. We developed the DTS-parameter to quantify the best rate-matched polar codes. Thereafter, we developed and formalized a recursive technique to design polarization kernels of higher order from component smaller order. A scaled version of the DTS-parameter, namely SDTS-parameter (denoted by the symbol ζ in this article) was used for the analytical assessment of this construction technique and validated for single-kernel polar codes. In this paper, we aim to extend the analysis of the aforementioned SDTS parameter for multi-kernel polar codes and validate their applicability in this domain as well.

Cited by 1Open access

Operation of Faddeev-Kernel in Configuration Space

Ishikawa, S. · arXiv (Cornell University) · 2007

We present a practical method to solve Faddeev three-body equations at energies above three-body breakup threshold as integral equations in coordinate space. This is an extension of previously used method for bound states and scattering states below three-body breakup threshold energy. We show that breakup components in three-body reactions produce long-range effects on Faddeev integral kernels in coordinate space, and propose numerical procedures to treat these effects. Using these techniques, we solve Faddeev equations for neutron-deuteron scattering to compare with benchmark solutions.

Cited by 0Open access

Robust Stochastic Configuration Networks with Kernel Density Estimation

Dianhui Wang, Ming Li · arXiv (Cornell University) · 2017

Neural networks have been widely used as predictive models to fit data distribution, and they could be implemented through learning a collection of samples. In many applications, however, the given dataset may contain noisy samples or outliers which may result in a poor learner model in terms of generalization. This paper contributes to a development of robust stochastic configuration networks (RSCNs) for resolving uncertain data regression problems. RSCNs are built on original stochastic configuration networks with weighted least squares method for evaluating the output weights, and the input weights and biases are incrementally and randomly generated by satisfying with a set of inequality constrains. The kernel density estimation (KDE) method is employed to set the penalty weights for each training samples, so that some negative impacts, caused by noisy data or outliers, on the resulting learner model can be reduced. The alternating optimization technique is applied for updating a RSCN model with improved penalty weights computed from the kernel density estimation function. Performance evaluation is carried out by a function approximation, four benchmark datasets and a case study on engineering application. Comparisons to other robust randomised neural modelling techniques, including the probabilistic robust learning algorithm for neural networks with random weights and improved RVFL networks, indicate that the proposed RSCNs with KDE perform favourably and demonstrate good potential for real-world applications.

Cited by 1Open access

Kernel Stochastic Configuration Networks for Industrial Data Regression

Yongxuan Chen, Dianhui Wang · IEEE Transactions on Industrial Informatics · 2025

Stochastic configuration networks (SCNs) are featured by its way of random parameters assignment in the light of a supervisory mechanism, resulting in the universal approximation property at algorithmic level. This article presents a kernel version of SCNs, termed KSCNs, aiming to enhance model’s representation learning capability and performance stability. The random bases of a built SCN model can be used to span a reproducing kernel Hilbert space, followed by our proposed algorithm for constructing KSCNs. It is shown that the data distribution in the reconstructive space is favorable for regression solving and the proposed KSCNs hold the universal approximation property. Three industrial datasets are used in this study for performance evaluation. Experimental results with comparisons against existing solutions clearly demonstrate that the proposed KSCNs remarkably outperform the original SCNs and some typical kernel methods for resolving industrial data regression problems.

Cited by 0

Improving Usability of the Linux Kernel Configuration Tools

Kacper Bak · 2013

Tailoring a Linux kernel to one’s needs has been one of the most cumbersome tasks a GNU/Linux user can do. There have been many attempts to overcome this problem by introducing smarter configuration tools. Those tools, however, still lack some important features which discourages users from using them. In this project, we address the problem of usability of the Linux kernel configuration tools. We identify the major usability issues with current tools, propose a better user interface, and evaluate it on a group of Linux enthusiasts. Author Keywords usability, linux, kernel, configuration

Cited by 0

KconfigTune: Automatic Performance Tuning for Linux Kernel Configuration

Ying Sun, Fangqi Bi, J. C. He et al. · IEEE Transactions on Computers · 2025

The Linux kernel offers nearly 20,000 configuration options, making it highly customizable but also extremely challenging to manually optimize for performance. The diversity of operating environments and workloads further limits the effectiveness of static or expert-crafted configurations. This paper introduces KconfigTune, an automated tuning system that jointly optimizes Linux kernel configuration options and system-level parameters (e.g., procfs entries). We propose extconfig to expand the parameter search space and, for the first time, address configuration dependencies that arise during automated tuning. To ensure valid and bootable kernels, we design a dependency automatic fix tool and a GRUB-based mechanism. KconfigTune models the tuning process as a machine learning problem and applies Bayesian optimization with a random forest model to efficiently explore the vast and interdependent configuration space. Unlike prior approaches that focus solely on either kernel options or system parameters, KconfigTune achieves deeper integration by jointly tuning both compile-time and runtime behaviors. Experimental results show significant performance improvements, with gains of 18.62% and 19.92% over the default configuration in UnixBench and LEBench tests, respectively. Compared to the state-of-the-art, KconfigTune outperforms by 2.9% and 12.69% in these benchmarks. Ablation studies further confirm that these gains primarily stem from the combined tuning of kernel configurations and system-level parameters.

Cited by 0

ConfigFix: Interactive Configuration Conflict Resolution for the Linux Kernel

Patrick Franz, Thorsten Berger, Ibrahim Fayaz et al. · arXiv (Cornell University) · 2021

Highly configurable systems are highly complex systems. The Linux kernel is arguably one of the most well-known examples. Given its vast configuration space, researchers have used it to conduct many empirical studies as well as to build dedicated methods and tools for analyzing, configuring, testing, optimizing, and maintaining the kernel. However, despite a large body of work, mainly bug fixes that were the result of such research made it back into the kernel's source tree. Unfortunately, Linux users still struggle with kernel configuration and resolving configuration conflicts, since the kernel largely lacks automated support. Additionally, there are technical and community requirements for supporting automated conflict resolution in the kernel, for example, using a pure C-based solution that uses only compatible third-party libraries (if any). With the aim of contributing back to the Linux community, we present ConfigFix, a tooling that we integrated with the Linux kernel configurator, that is purely implemented in C, and that is finally a working solution able to produce fixes for configuration conflicts. We describe our experiences of building upon the large body of research done on the kernel configuration mechanisms as well as how we designed and realized ConfigFix while adhering to the Linux kernel's community requirements and standards. ConfigFix not only helps Linux kernel users obtain their desired configuration, but our implemented semantic abstraction provides the basis for many of the above techniques supporting kernel configuration.

Cited by 1Open access

Fuzzy Kernel Stochastic Configuration Networks for Industrial Data Modeling

Yongxuan Chen, Dianhui Wang · IEEE Transactions on Fuzzy Systems · 2025

Kernel stochastic configuration networks (KSCNs) belong to the randomized learner model with universal approximation property. However, the original KSCNs model lacks logical reasoning ability and the utilization of prior knowledge. This article proposes a novel neurofuzzy learner model based on KSCNs, termed fuzzy KSCNs (F-KSCNs), for enhancing model’s interpretability and modeling performance. First, the Takagi–Sugeno–Kang fuzzy inference system is integrated into the modeling process of KSCNs, where multiple submodels are constructed according to the associated fuzzy rules. Then, the parameters of F-KSCNs are determined by the kernel stochastic configuration algorithm to guarantee model’s universal approximation property. Finally, the output of F-KSCNs is obtained by comprehensively evaluating the significance of each submodel. Compared to the original KSCNs, the proposed F-KSCNs have stronger fuzzy inference ability and can deal with rule-based information. Moreover, computational efficiency is greatly improved under this fuzzy inference framework. Two industrial case studies are carried out for performance evaluation. The experimental results with comparisons with existing solutions clearly show the superiorities of our proposed method, including generalization performance and modeling efficiency.

Cited by 0

Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration

Xiaoxiao Wang, Xueying Guo, Jie Chuai et al. · arXiv (Cornell University) · 2018

Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches have great potentials to automate and optimize network configuration. Learning-based approaches face the challenges of learning a highly complex function for each base station and balancing the fundamental exploration-exploitation tradeoff while minimizing the exploration cost. Fortunately, in cellular networks, base stations (BSs) often have similarities even though they are not identical. To leverage such similarities, we propose kernel-based multi-BS contextual bandit algorithm based on multi-task learning. In the algorithm, we leverage the similarity among different BSs defined by conditional kernel embedding. We present theoretical analysis of the proposed algorithm in terms of regret and multi-task-learning efficiency. We evaluate the effectiveness of our algorithm based on a simulator built by real traces.

Cited by 0Open access

ConfigFix: Interactive Configuration Conflict Resolution for the Linux\n Kernel

P. Franz, Thorsten Berger, Ibrahim Fayaz et al. · arXiv (Cornell University) · 2020

Highly configurable systems are highly complex systems, with the Linux kernel\narguably being one of the most well-known ones. Since 2007, it has been a\nfrequent target of the research community, conducting empirical studies and\nbuilding dedicated methods and tools for analyzing, configuring, testing,\noptimizing, and maintaining the kernel in the light of its vast configuration\nspace. However, despite a large body of work, mainly bug fixes that were the\nresult of such research made it back into the kernel's source tree.\nUnfortunately, Linux users still struggle with kernel configuration and\nresolving configuration conflicts, since the kernel largely lacks automated\nsupport. Additionally, there are technical and community requirements for\nsupporting automated conflict resolution in the kernel, such as, for example,\nusing a pure C-based solution that uses only compatible third-party libraries\n(if any). With the aim of contributing back to the Linux community, we present\nCONFIGFIX, a tooling that we integrated with the kernel configurator, that is\npurely implemented in C, and that is finally a working solution able to produce\nfixes for configuration conflicts. In this experience report, we describe our\nexperiences ranging over a decade of building upon the large body of work from\nresearch on the Linux kernel configuration mechanisms as well as how we\ndesigned and realized CONFIGFIX while adhering to the Linux kernel's community\nrequirements and standards. While CONFIGFIX helps Linux kernel users obtaining\ntheir desired configuration, the sound semantic abstraction we implement\nprovides the basis for many of the above techniques supporting kernel\nconfiguration, helping researchers and kernel developers.\n

Cited by 1Open access